def Generator(restore_path, voc_path, csv_num, gen_num, mol_num):
restore_from = restore_path
# Read vocabulary from a file
voc = Vocabulary(init_from_file=voc_path)
for n in range(0, csv_num):
Prior = RNN(voc)
if restore_from:
Prior.rnn.load_state_dict(torch.load(restore_from))
seqs, likelihood, _ = Prior.sample(gen_num)
valid = 0
smiles = []
val_smi = []
for i, seq in enumerate(seqs.cpu().numpy()):
smile = voc.decode(seq)
smiles.append(smile)
if Chem.MolFromSmiles(smile):
valid += 1
val_smi.append(smile)
if i < mol_num:
print(smile)
Val_s = pd.DataFrame(data=val_smi, columns=['smiles'])
Val_s.to_csv('./model/data_gen_' + str(n) + '.csv', index=False)
print(valid)
gc.collect()
def pretrain(restore_from=None):
"""Trains the Prior RNN"""
# Read vocabulary from a file
voc = Vocabulary(init_from_file="data/Voc")
# Create a Dataset from a SMILES file
moldata = MolData("data/mols_filtered.smi", voc)
data = DataLoader(moldata,
batch_size=128,
shuffle=True,
drop_last=True,
collate_fn=MolData.collate_fn)
Prior = RNN(voc)
# Can restore from a saved RNN
if restore_from:
Prior.rnn.load_state_dict(torch.load(restore_from))
optimizer = torch.optim.Adam(Prior.rnn.parameters(), lr=0.001)
for epoch in range(1, 6):
# When training on a few million compounds, this model converges
# in a few of epochs or even faster. If model sized is increased
# its probably a good idea to check loss against an external set of
# validation SMILES to make sure we dont overfit too much.
for step, batch in tqdm(enumerate(data), total=len(data)):
# Sample from DataLoader
seqs = batch.long()
# Calculate loss
log_p, _ = Prior.likelihood(seqs)
loss = -log_p.mean()
# Calculate gradients and take a step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Every 500 steps we decrease learning rate and print some information
if step % 500 == 0 and step != 0 and False:
decrease_learning_rate(optimizer, decrease_by=0.03)
tqdm.write("*" * 50)
tqdm.write(
"Epoch {:3d} step {:3d} loss: {:5.2f}\n".format(
epoch, step, loss.data[0]))
seqs, likelihood, _ = Prior.sample(128)
valid = 0
for i, seq in enumerate(seqs.cpu().numpy()):
smile = voc.decode(seq)
if Chem.MolFromSmiles(smile):
valid += 1
if i < 5:
tqdm.write(smile)
tqdm.write("\n{:>4.1f}% valid SMILES".format(100 * valid /
len(seqs)))
tqdm.write("*" * 50 + "\n")
def train_model():
"""Do transfer learning for generating SMILES"""
voc = Vocabulary(init_from_file='data/Voc')
cano_smi_file('refined_smii.csv', 'refined_smii_cano.csv')
moldata = MolData('refined_smii_cano.csv', voc)
# Monomers 67 and 180 were removed because of the unseen [C-] in voc
# DAs containing [se] [SiH2] [n] removed: 38 molecules
data = DataLoader(moldata,
batch_size=64,
shuffle=True,
drop_last=False,
collate_fn=MolData.collate_fn)
transfer_model = RNN(voc)
if torch.cuda.is_available():
transfer_model.rnn.load_state_dict(torch.load('data/Prior.ckpt'))
else:
transfer_model.rnn.load_state_dict(
torch.load('data/Prior.ckpt',
map_location=lambda storage, loc: storage))
# for param in transfer_model.rnn.parameters():
# param.requires_grad = False
optimizer = torch.optim.Adam(transfer_model.rnn.parameters(), lr=0.001)
for epoch in range(1, 10):
for step, batch in tqdm(enumerate(data), total=len(data)):
seqs = batch.long()
log_p, _ = transfer_model.likelihood(seqs)
loss = -log_p.mean()
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 5 == 0 and step != 0:
decrease_learning_rate(optimizer, decrease_by=0.03)
tqdm.write('*' * 50)
tqdm.write(
"Epoch {:3d} step {:3d} loss: {:5.2f}\n".format(
epoch, step, loss.data[0]))
seqs, likelihood, _ = transfer_model.sample(128)
valid = 0
for i, seq in enumerate(seqs.cpu().numpy()):
smile = voc.decode(seq)
if Chem.MolFromSmiles(smile):
valid += 1
if i < 5:
tqdm.write(smile)
tqdm.write("\n{:>4.1f}% valid SMILES".format(100 * valid /
len(seqs)))
tqdm.write("*" * 50 + '\n')
torch.save(transfer_model.rnn.state_dict(),
"data/transfer_model2.ckpt")
torch.save(transfer_model.rnn.state_dict(),
"data/transfer_modelw.ckpt")
def main(voc_file='data/Voc',
restore_model_from='data/Prior.ckpt',
output_file='data/Prior_10k.smi',
sample_size=10000):
voc = Vocabulary(init_from_file=voc_file)
print("Setting up networks")
Agent = RNN(voc)
if torch.cuda.is_available():
print("Cuda available, loading prior & agent")
Agent.rnn.load_state_dict(torch.load(restore_model_from))
else:
raise 'Cuda not available'
SMILES = []
for n in tqdm(range(sample_size//100), total=sample_size//100):
# Sample from Agent
seqs, agent_likelihood, entropy = Agent.sample(100)
# Remove duplicates, ie only consider unique seqs
unique_idxs = unique(seqs)
seqs = seqs[unique_idxs]
agent_likelihood = agent_likelihood[unique_idxs]
entropy = entropy[unique_idxs]
smiles = seq_to_smiles(seqs, voc)
SMILES += smiles
if not os.path.exists(os.path.dirname(output_file)):
os.makedirs(os.path.dirname(output_file))
with open(output_file, "wt") as f:
[f.write(smi + '\n') for smi in SMILES]
return
def generate_smiles(n_smiles=500,
restore_from="data/Prior.ckpt",
voc_file="data/Voc",
embedding_size=128):
"""
This function takes a checkpoint for a trained RNN and the vocabulary file and generates n_smiles new smiles strings.
"""
n = 32
n_smiles = n_smiles - n_smiles % n
print("Generating %i smiles" % n_smiles)
voc = Vocabulary(init_from_file=voc_file)
generator = RNN(voc, embedding_size)
if torch.cuda.is_available():
generator.rnn.load_state_dict(torch.load(restore_from))
else:
generator.rnn.load_state_dict(
torch.load(restore_from,
map_location=lambda storage, loc: storage))
all_smiles = []
for i in range(int(n_smiles / n)):
sequences, _, _ = generator.sample(n)
smiles = seq_to_smiles(sequences, voc)
all_smiles += smiles
# Freeing up memory
del generator
torch.cuda.empty_cache()
return all_smiles
def black_box(load_weights='./data/Prior.ckpt', batch_size=1):
# Read vocabulary from a file
voc = Vocabulary(init_from_file="data/Voc")
vec_file = "data/vecs.dat"
_, mew, std = get_latent_vector(None, vec_file, moments=True)
vector = np.array([4.2619, 214.96, 512.07, 0.0, 1.0, 0.088, 7.0, 5.0, 100.01, 60.95, 7.0, 5.0, 2.0, 2.0, 2.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 9.0, 10.0, 0.0, 4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 23.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 3.0, 0.0, 0.0, 0.0, 34.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 8.0, 0.0, 0.0, 2.0, 3.0, 0.0, 2.0, 0.0, 9.0, 0.0, 0.0, 9.0, 0.0, 0.0, 0.0, 0.0, 8.0, 9.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 1.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 1.0, 0.0, 6.0, 0.0, 2.0, 0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 3.0, 28.0, 1.0, 5.0, 0.0, 2.0, 10.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.0, 0.0, 2.0, 6.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 1.0, 0.0, 2.0, 0.0, 3.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 8.0, 1.0, 0.0, 4.0, 0.0, 0.0, 0.0, 2.0, 5.0, 0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 5.0, 0.0, 5.0, 7.0, 4.0, 2.0, 0.0, 16.0, 20.0, 43.0, 83.0, 90.0, 23.0, 8.0, 37.0, 5.0, 24.0, 5.0, 4.0, 16.0, 5.0, 25.0, 93.0, 92.0, 38.0, 0.0, 0.0, 0.0, 4.0])
vector = (vector - mew) / std
data = [vector]
Prior = RNN(voc, len(data[0]))
# By default restore Agent to same model as Prior, but can restore from already trained Agent too.
# Saved models are partially on the GPU, but if we dont have cuda enabled we can remap these
# to the CPU.
if torch.cuda.is_available():
Prior.rnn.load_state_dict(torch.load(load_weights))
else:
Prior.rnn.load_state_dict(torch.load(load_weights, map_location=lambda storage, loc: storage))
for test_vec in data:
print('Test vector {}'.format(test_vec))
test_vec = Variable(test_vec).float()
valid = 0
num_smi = 100
all_smi = []
for i in range(num_smi):
seqs, prior_likelihood, entropy = Prior.sample(batch_size, test_vec)
smiles = seq_to_smiles(seqs, voc)[0]
if Chem.MolFromSmiles(smiles):
valid += 1
all_smi.append(smiles)
for smi in all_smi:
print(smi)
print("\n{:>4.1f}% valid SMILES".format(100 * valid / len(range(num_smi))))
def hill_climbing(pattern=None,
restore_agent_from='data/Prior.ckpt',
scoring_function='tanimoto',
scoring_function_kwargs=None,
save_dir=None,
learning_rate=0.0005,
batch_size=64,
n_steps=10,
num_processes=0,
use_custom_voc="data/Voc"):
voc = Vocabulary(init_from_file=use_custom_voc)
start_time = time.time()
if pattern:
Agent = scaffold_constrained_RNN(voc)
else:
Agent = RNN(voc)
logger = VizardLog('data/logs')
if torch.cuda.is_available():
Agent.rnn.load_state_dict(torch.load(restore_agent_from))
else:
Agent.rnn.load_state_dict(
torch.load(restore_agent_from,
map_location=lambda storage, loc: storage))
optimizer = torch.optim.Adam(Agent.rnn.parameters(), lr=learning_rate)
# Scoring_function
scoring_function = get_scoring_function(scoring_function=scoring_function,
num_processes=num_processes,
**scoring_function_kwargs)
# For policy based RL, we normally train on-policy and correct for the fact that more likely actions
# occur more often (which means the agent can get biased towards them). Using experience replay is
# therefor not as theoretically sound as it is for value based RL, but it seems to work well.
experience = Experience(voc)
# Log some network weights that can be dynamically plotted with the Vizard bokeh app
logger.log(Agent.rnn.gru_2.weight_ih.cpu().data.numpy()[::100],
"init_weight_GRU_layer_2_w_ih")
logger.log(Agent.rnn.gru_2.weight_hh.cpu().data.numpy()[::100],
"init_weight_GRU_layer_2_w_hh")
logger.log(Agent.rnn.embedding.weight.cpu().data.numpy()[::30],
"init_weight_GRU_embedding")
logger.log(Agent.rnn.gru_2.bias_ih.cpu().data.numpy(),
"init_weight_GRU_layer_2_b_ih")
logger.log(Agent.rnn.gru_2.bias_hh.cpu().data.numpy(),
"init_weight_GRU_layer_2_b_hh")
# Information for the logger
step_score = [[], []]
print("Model initialized, starting training...")
for step in range(n_steps):
# Sample from Agent
if pattern:
seqs, agent_likelihood, entropy = Agent.sample(pattern, batch_size)
else:
seqs, agent_likelihood, entropy = Agent.sample(batch_size)
gc.collect()
# Remove duplicates, ie only consider unique seqs
unique_idxs = unique(seqs)
seqs = seqs[unique_idxs]
agent_likelihood = agent_likelihood[unique_idxs]
entropy = entropy[unique_idxs]
# Get prior likelihood and score
smiles = seq_to_smiles(seqs, voc)
score = scoring_function(smiles)
new_experience = zip(smiles, score, agent_likelihood)
experience.add_experience(new_experience)
indexes = np.flip(np.argsort(np.array(score)))
# Train the agent for 10 epochs on hill-climbing procedure
for epoch in range(10):
loss = Variable(torch.zeros(1))
counter = 0
seen_seqs = []
for j in indexes:
if counter > 50:
break
seq = seqs[j]
s = smiles[j]
if s not in seen_seqs:
seen_seqs.append(s)
log_p, _ = Agent.likelihood(Variable(seq).view(1, -1))
loss -= log_p.mean()
counter += 1
loss /= counter
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Print some information for this step
time_elapsed = (time.time() - start_time) / 3600
time_left = (time_elapsed * ((n_steps - step) / (step + 1)))
print(
"\n Step {} Fraction valid SMILES: {:4.1f} Time elapsed: {:.2f}h Time left: {:.2f}h"
.format(step,
fraction_valid_smiles(smiles) * 100, time_elapsed,
time_left))
print(" Agent Prior Target Score SMILES")
for i in range(10):
print(" {:6.2f} {}".format(score[i], smiles[i]))
# Need this for Vizard plotting
step_score[0].append(step + 1)
step_score[1].append(np.mean(score))
# Log some weights
logger.log(Agent.rnn.gru_2.weight_ih.cpu().data.numpy()[::100],
"weight_GRU_layer_2_w_ih")
logger.log(Agent.rnn.gru_2.weight_hh.cpu().data.numpy()[::100],
"weight_GRU_layer_2_w_hh")
logger.log(Agent.rnn.embedding.weight.cpu().data.numpy()[::30],
"weight_GRU_embedding")
logger.log(Agent.rnn.gru_2.bias_ih.cpu().data.numpy(),
"weight_GRU_layer_2_b_ih")
logger.log(Agent.rnn.gru_2.bias_hh.cpu().data.numpy(),
"weight_GRU_layer_2_b_hh")
logger.log("\n".join([smiles + "\t" + str(round(score, 2)) for smiles, score in zip \
(smiles[:12], score[:12])]), "SMILES", dtype="text", overwrite=True)
logger.log(np.array(step_score), "Scores")
# If the entire training finishes, we create a new folder where we save this python file
# as well as some sampled sequences and the contents of the experinence (which are the highest
# scored sequences seen during training)
if not save_dir:
save_dir = 'data/results/run_' + time.strftime("%Y-%m-%d-%H_%M_%S",
time.localtime())
try:
os.makedirs(save_dir)
except:
print("Folder already existing... overwriting previous results")
copyfile('train_agent.py', os.path.join(save_dir, "train_agent.py"))
experience.print_memory(os.path.join(save_dir, "memory"))
torch.save(Agent.rnn.state_dict(), os.path.join(save_dir, 'Agent.ckpt'))
previous_smiles = []
with open(os.path.join(save_dir, "memory.smi"), 'w') as f:
for i, exp in enumerate(experience.memory):
try:
if Chem.MolToSmiles(
Chem.rdmolops.RemoveStereochemistry(
Chem.MolFromSmiles(
exp[0]))) not in previous_smiles:
f.write("{}\n".format(exp[0]))
previous_smiles.append(
Chem.MolToSmiles(
Chem.rdmolops.RemoveStereochemistry(
Chem.MolFromSmiles(exp[0]))))
except:
pass
def train_agent(restore_prior_from='data/Prior.ckpt',
restore_agent_from='data/Prior.ckpt',
scoring_function='tanimoto',
scoring_function_kwargs=None,
save_dir=None,
learning_rate=0.0005,
batch_size=64,
n_steps=3000,
num_processes=0,
sigma=60,
experience_replay=0):
voc = Vocabulary(init_from_file="data/Voc")
start_time = time.time()
Prior = RNN(voc)
Agent = RNN(voc)
logger = VizardLog('data/logs')
# By default restore Agent to same model as Prior, but can restore from already trained Agent too.
# Saved models are partially on the GPU, but if we dont have cuda enabled we can remap these
# to the CPU.
if torch.cuda.is_available():
Prior.rnn.load_state_dict(torch.load(restore_prior_from))
Agent.rnn.load_state_dict(torch.load(restore_agent_from))
else:
Prior.rnn.load_state_dict(
torch.load(restore_prior_from,
map_location=lambda storage, loc: storage))
Agent.rnn.load_state_dict(
torch.load(restore_agent_from,
map_location=lambda storage, loc: storage))
# We dont need gradients with respect to Prior
for param in Prior.rnn.parameters():
param.requires_grad = False
optimizer = torch.optim.Adam(Agent.rnn.parameters(), lr=0.0005)
# Scoring_function
scoring_function = get_scoring_function(scoring_function=scoring_function,
num_processes=num_processes,
**scoring_function_kwargs)
# For policy based RL, we normally train on-policy and correct for the fact that more likely actions
# occur more often (which means the agent can get biased towards them). Using experience replay is
# therefor not as theoretically sound as it is for value based RL, but it seems to work well.
experience = Experience(voc)
# Log some network weights that can be dynamically plotted with the Vizard bokeh app
logger.log(Agent.rnn.gru_2.weight_ih.cpu().data.numpy()[::100],
"init_weight_GRU_layer_2_w_ih")
logger.log(Agent.rnn.gru_2.weight_hh.cpu().data.numpy()[::100],
"init_weight_GRU_layer_2_w_hh")
logger.log(Agent.rnn.embedding.weight.cpu().data.numpy()[::30],
"init_weight_GRU_embedding")
logger.log(Agent.rnn.gru_2.bias_ih.cpu().data.numpy(),
"init_weight_GRU_layer_2_b_ih")
logger.log(Agent.rnn.gru_2.bias_hh.cpu().data.numpy(),
"init_weight_GRU_layer_2_b_hh")
# Information for the logger
step_score = [[], []]
print("Model initialized, starting training...")
for step in range(n_steps):
# Sample from Agent
seqs, agent_likelihood, entropy = Agent.sample(batch_size)
# Remove duplicates, ie only consider unique seqs
unique_idxs = unique(seqs)
seqs = seqs[unique_idxs]
agent_likelihood = agent_likelihood[unique_idxs]
entropy = entropy[unique_idxs]
# Get prior likelihood and score
prior_likelihood, _ = Prior.likelihood(Variable(seqs))
smiles = seq_to_smiles(seqs, voc)
score = scoring_function(smiles)
# Calculate augmented likelihood
augmented_likelihood = prior_likelihood + sigma * Variable(score)
loss = torch.pow((augmented_likelihood - agent_likelihood), 2)
# Experience Replay
# First sample
if experience_replay and len(experience) > 4:
exp_seqs, exp_score, exp_prior_likelihood = experience.sample(4)
exp_agent_likelihood, exp_entropy = Agent.likelihood(
exp_seqs.long())
exp_augmented_likelihood = exp_prior_likelihood + sigma * exp_score
exp_loss = torch.pow(
(Variable(exp_augmented_likelihood) - exp_agent_likelihood), 2)
loss = torch.cat((loss, exp_loss), 0)
agent_likelihood = torch.cat(
(agent_likelihood, exp_agent_likelihood), 0)
# Then add new experience
prior_likelihood = prior_likelihood.data.cpu().numpy()
new_experience = zip(smiles, score, prior_likelihood)
experience.add_experience(new_experience)
# Calculate loss
loss = loss.mean()
# Add regularizer that penalizes high likelihood for the entire sequence
loss_p = -(1 / agent_likelihood).mean()
loss += 5 * 1e3 * loss_p
# Calculate gradients and make an update to the network weights
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Convert to numpy arrays so that we can print them
augmented_likelihood = augmented_likelihood.data.cpu().numpy()
agent_likelihood = agent_likelihood.data.cpu().numpy()
# Print some information for this step
time_elapsed = (time.time() - start_time) / 3600
time_left = (time_elapsed * ((n_steps - step) / (step + 1)))
print(
"\n Step {} Fraction valid SMILES: {:4.1f} Time elapsed: {:.2f}h Time left: {:.2f}h"
.format(step,
fraction_valid_smiles(smiles) * 100, time_elapsed,
time_left))
print(" Agent Prior Target Score SMILES")
for i in range(10):
print(" {:6.2f} {:6.2f} {:6.2f} {:6.2f} {}".format(
agent_likelihood[i], prior_likelihood[i],
augmented_likelihood[i], score[i], smiles[i]))
# Need this for Vizard plotting
step_score[0].append(step + 1)
step_score[1].append(np.mean(score))
# Log some weights
logger.log(Agent.rnn.gru_2.weight_ih.cpu().data.numpy()[::100],
"weight_GRU_layer_2_w_ih")
logger.log(Agent.rnn.gru_2.weight_hh.cpu().data.numpy()[::100],
"weight_GRU_layer_2_w_hh")
logger.log(Agent.rnn.embedding.weight.cpu().data.numpy()[::30],
"weight_GRU_embedding")
logger.log(Agent.rnn.gru_2.bias_ih.cpu().data.numpy(),
"weight_GRU_layer_2_b_ih")
logger.log(Agent.rnn.gru_2.bias_hh.cpu().data.numpy(),
"weight_GRU_layer_2_b_hh")
logger.log("\n".join([smiles + "\t" + str(round(score, 2)) for smiles, score in zip \
(smiles[:12], score[:12])]), "SMILES", dtype="text", overwrite=True)
logger.log(np.array(step_score), "Scores")
# If the entire training finishes, we create a new folder where we save this python file
# as well as some sampled sequences and the contents of the experinence (which are the highest
# scored sequences seen during training)
if not save_dir:
save_dir = 'data/results/run_' + time.strftime("%Y-%m-%d-%H_%M_%S",
time.localtime())
os.makedirs(save_dir)
copyfile('train_agent.py', os.path.join(save_dir, "train_agent.py"))
experience.print_memory(os.path.join(save_dir, "memory"))
torch.save(Agent.rnn.state_dict(), os.path.join(save_dir, 'Agent.ckpt'))
seqs, agent_likelihood, entropy = Agent.sample(256)
prior_likelihood, _ = Prior.likelihood(Variable(seqs))
prior_likelihood = prior_likelihood.data.cpu().numpy()
smiles = seq_to_smiles(seqs, voc)
score = scoring_function(smiles)
with open(os.path.join(save_dir, "sampled"), 'w') as f:
f.write("SMILES Score PriorLogP\n")
for smiles, score, prior_likelihood in zip(smiles, score,
prior_likelihood):
f.write("{} {:5.2f} {:6.2f}\n".format(smiles, score,
prior_likelihood))
def train_model(voc_dir,
smi_dir,
prior_dir,
tf_dir,
tf_process_dir,
freeze=False):
"""
Transfer learning on target molecules using the SMILES structures
Args:
voc_dir: location of the vocabulary
smi_dir: location of the SMILES file used for transfer learning
prior_dir: location of prior trained model to initialize transfer learning
tf_dir: location to save the transfer learning model
tf_process_dir: location to save the SMILES sampled while doing transfer learning
freeze: Bool. If true, all parameters in the RNN will be frozen except for the last linear layer during
transfer learning.
Returns: None
"""
voc = Vocabulary(init_from_file=voc_dir)
#cano_smi_file('all_smi_refined.csv', 'all_smi_refined_cano.csv')
moldata = MolData(smi_dir, voc)
# Monomers 67 and 180 were removed because of the unseen [C-] in voc
# DAs containing [C] removed: 43 molecules in 5356; Ge removed: 154 in 5356; [c] removed 4 in 5356
# [S] 1 molecule in 5356
data = DataLoader(moldata,
batch_size=64,
shuffle=True,
drop_last=False,
collate_fn=MolData.collate_fn)
transfer_model = RNN(voc)
# if freeze=True, freeze all parameters except those in the linear layer
if freeze:
for param in transfer_model.rnn.parameters():
param.requires_grad = False
transfer_model.rnn.linear = nn.Linear(512, voc.vocab_size)
if torch.cuda.is_available():
transfer_model.rnn.load_state_dict(torch.load(prior_dir))
else:
transfer_model.rnn.load_state_dict(
torch.load(prior_dir, map_location=lambda storage, loc: storage))
optimizer = torch.optim.Adam(transfer_model.rnn.parameters(), lr=0.0005)
smi_lst = []
epoch_lst = []
for epoch in range(1, 11):
for step, batch in tqdm(enumerate(data), total=len(data)):
seqs = batch.long()
log_p, _ = transfer_model.likelihood(seqs)
loss = -log_p.mean()
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 80 == 0 and step != 0:
decrease_learning_rate(optimizer, decrease_by=0.03)
tqdm.write('*' * 50)
tqdm.write(
"Epoch {:3d} step {:3d} loss: {:5.2f}\n".format(
epoch, step, loss.data[0]))
seqs, likelihood, _ = transfer_model.sample(128)
valid = 0
for i, seq in enumerate(seqs.cpu().numpy()):
smile = voc.decode(seq)
if Chem.MolFromSmiles(smile):
valid += 1
if i < 5:
tqdm.write(smile)
tqdm.write("\n{:>4.1f}% valid SMILES".format(100 * valid /
len(seqs)))
tqdm.write("*" * 50 + '\n')
torch.save(transfer_model.rnn.state_dict(), tf_dir)
seqs, likelihood, _ = transfer_model.sample(1024)
valid = 0
#valid_smis = []
for i, seq in enumerate(seqs.cpu().numpy()):
smile = voc.decode(seq)
if Chem.MolFromSmiles(smile):
try:
AllChem.GetMorganFingerprintAsBitVect(
Chem.MolFromSmiles(smile), 2, 1024)
valid += 1
smi_lst.append(smile)
epoch_lst.append(epoch)
except:
continue
torch.save(transfer_model.rnn.state_dict(), tf_dir)
transfer_process_df = pd.DataFrame(columns=['SMILES', 'Epoch'])
transfer_process_df['SMILES'] = pd.Series(data=smi_lst)
transfer_process_df['Epoch'] = pd.Series(data=epoch_lst)
transfer_process_df.to_csv(tf_process_dir)
def sample_smiles(voc_dir, nums, outfn, tf_dir, until=False):
"""Sample smiles using the transferred model"""
voc = Vocabulary(init_from_file=voc_dir)
transfer_model = RNN(voc)
output = open(outfn, 'w')
if torch.cuda.is_available():
transfer_model.rnn.load_state_dict(torch.load(tf_dir))
else:
transfer_model.rnn.load_state_dict(
torch.load(tf_dir, map_location=lambda storage, loc: storage))
for param in transfer_model.rnn.parameters():
param.requires_grad = False
if not until:
seqs, likelihood, _ = transfer_model.sample(nums)
valid = 0
double_br = 0
unique_idx = unique(seqs)
seqs = seqs[unique_idx]
for i, seq in enumerate(seqs.cpu().numpy()):
smile = voc.decode(seq)
if Chem.MolFromSmiles(smile):
try:
AllChem.GetMorganFingerprintAsBitVect(
Chem.MolFromSmiles(smile), 2, 1024)
valid += 1
output.write(smile + '\n')
except:
continue
#if smile.count('Br') == 2:
# double_br += 1
#output.write(smile+'\n')
tqdm.write(
'\n{} molecules sampled, {} valid SMILES, {} with double Br'.
format(nums, valid, double_br))
output.close()
else:
valid = 0
n_sample = 0
while valid < nums:
seq, likelihood, _ = transfer_model.sample(1)
n_sample += 1
seq = seq.cpu().numpy()
seq = seq[0]
# print(seq)
smile = voc.decode(seq)
if Chem.MolFromSmiles(smile):
try:
AllChem.GetMorganFingerprintAsBitVect(
Chem.MolFromSmiles(smile), 2, 1024)
valid += 1
output.write(smile + '\n')
#if valid % 100 == 0 and valid != 0:
# tqdm.write('\n{} valid molecules sampled, with {} of total samples'.format(valid, n_sample))
except:
continue
tqdm.write(
'\n{} valid molecules sampled, with {} of total samples'.format(
nums, n_sample))
def train_agent(restore_prior_from='data/Prior.ckpt',
restore_agent_from='data/Prior.ckpt',
voc_file='data/Voc',
molscore_config=None,
learning_rate=0.0005,
batch_size=64, n_steps=3000, sigma=60,
experience_replay=0):
voc = Vocabulary(init_from_file=voc_file)
start_time = time.time()
# Scoring_function
scoring_function = MolScore(molscore_config)
scoring_function.log_parameters({'batch_size': batch_size, 'sigma': sigma})
print("Building RNNs")
Prior = RNN(voc)
Agent = RNN(voc)
# By default restore Agent to same model as Prior, but can restore from already trained Agent too.
# Saved models are partially on the GPU, but if we dont have cuda enabled we can remap these
# to the CPU.
if torch.cuda.is_available():
print("Cuda available, loading prior & agent")
Prior.rnn.load_state_dict(torch.load(restore_prior_from))
Agent.rnn.load_state_dict(torch.load(restore_agent_from))
else:
print("Cuda not available, remapping to cpu")
Prior.rnn.load_state_dict(torch.load(restore_prior_from, map_location=lambda storage, loc: storage))
Agent.rnn.load_state_dict(torch.load(restore_agent_from, map_location=lambda storage, loc: storage))
# We dont need gradients with respect to Prior
for param in Prior.rnn.parameters():
param.requires_grad = False
optimizer = torch.optim.Adam(Agent.rnn.parameters(), lr=learning_rate)
# For logging purposes let's save some training parameters not captured by molscore
with open(os.path.join(scoring_function.save_dir, 'reinvent_parameters.txt'), 'wt') as f:
[f.write(f'{p}: {v}\n') for p, v in {'learning_rate': learning_rate, 'batch_size': batch_size,
'n_steps': n_steps, 'sigma': sigma,
'experience_replay': experience_replay}.items()]
# For policy based RL, we normally train on-policy and correct for the fact that more likely actions
# occur more often (which means the agent can get biased towards them). Using experience replay is
# therefore not as theoretically sound as it is for value based RL, but it seems to work well.
experience = Experience(voc)
print("Model initialized, starting training...")
for step in range(n_steps):
# Sample from Agent
seqs, agent_likelihood, entropy = Agent.sample(batch_size)
# Remove duplicates, ie only consider unique seqs
unique_idxs = unique(seqs)
seqs = seqs[unique_idxs]
agent_likelihood = agent_likelihood[unique_idxs]
entropy = entropy[unique_idxs]
# Get prior likelihood and score
prior_likelihood, _ = Prior.likelihood(Variable(seqs))
smiles = seq_to_smiles(seqs, voc)
# Using molscore instead here
try:
score = scoring_function(smiles, step=step)
augmented_likelihood = prior_likelihood + sigma * Variable(score)
except: # If anything goes wrong with molscore, write scores and save .ckpt and kill monitor
with open(os.path.join(scoring_function.save_dir,
f'failed_smiles_{scoring_function.step}.smi'), 'wt') as f:
[f.write(f'{smi}\n') for smi in smiles]
torch.save(Agent.rnn.state_dict(),
os.path.join(scoring_function.save_dir, f'Agent_{step}.ckpt'))
scoring_function.write_scores()
scoring_function.kill_dash_monitor()
raise
# Calculate loss
loss = torch.pow((augmented_likelihood - agent_likelihood), 2)
# Experience Replay
# First sample
if experience_replay and len(experience)>4:
exp_seqs, exp_score, exp_prior_likelihood = experience.sample(4)
exp_agent_likelihood, exp_entropy = Agent.likelihood(exp_seqs.long())
exp_augmented_likelihood = exp_prior_likelihood + sigma * exp_score
exp_loss = torch.pow((Variable(exp_augmented_likelihood) - exp_agent_likelihood), 2)
loss = torch.cat((loss, exp_loss), 0)
agent_likelihood = torch.cat((agent_likelihood, exp_agent_likelihood), 0)
# Then add new experience
prior_likelihood = prior_likelihood.data.cpu().numpy()
new_experience = zip(smiles, score, prior_likelihood)
experience.add_experience(new_experience)
# Calculate loss
loss = loss.mean()
# Add regularizer that penalizes high likelihood for the entire sequence
loss_p = - (1 / agent_likelihood).mean()
loss += 5 * 1e3 * loss_p
# Calculate gradients and make an update to the network weights
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Convert to numpy arrays so that we can print them
augmented_likelihood = augmented_likelihood.data.cpu().numpy()
agent_likelihood = agent_likelihood.data.cpu().numpy()
# Print some information for this step
time_elapsed = (time.time() - start_time) / 3600
time_left = (time_elapsed * ((n_steps - step) / (step + 1)))
print(f"\n Step {step} Fraction valid SMILES: {fraction_valid_smiles(smiles) * 100:4.1f}\
Time elapsed: {time_elapsed:.2f}h Time left: {time_left:.2f}h")
print(" Agent Prior Target Score SMILES")
for i in range(10):
print(f" {agent_likelihood[i]:6.2f} {prior_likelihood[i]:6.2f} {augmented_likelihood[i]:6.2f} {score[i]:6.2f} {smiles[i]}")
# Save the agent weights every 250 iterations ####
if step % 250 == 0 and step != 0:
torch.save(Agent.rnn.state_dict(),
os.path.join(scoring_function.save_dir, f'Agent_{step}.ckpt'))
# If the entire training finishes, write out MolScore dataframe, kill dash_utils monitor and
# save the final Agent.ckpt
torch.save(Agent.rnn.state_dict(), os.path.join(scoring_function.save_dir, f'Agent_{n_steps}.ckpt'))
scoring_function.write_scores()
scoring_function.kill_dash_monitor()
return
from model import RNN
from data_structs import Vocabulary, Experience
from scoring_functions import get_scoring_function
from util import Variable, seq_to_smiles, fraction_valid_smiles, unique
from vizard_logger import VizardLog
def train_agent(restore_prior_from='data/Prior.ckpt',
restore_agent_from='data/Prior.ckpt',
scoring_function='tanimoto',
scoring_function_kwargs=None,
save_dir=None, learning_rate=0.0005,
batch_size=64, n_steps=3000,
num_processes=0, sigma=60,
experience_replay=0
)
voc = Vocabulary(init_from_file='data/Voc')
start_time = time.time()
Prior = RNN(voc)
Agent = RNN(voc)
logger = VizardLog('data/logs')
# By default restore Agent to same model as Prior, but can restore from already trained Agent too.
# Saved models are partially on the GPU, but if we dont have cuda enabled we can remap these
# to the CPU.
if torch.cuda.is_available():
Prior.rnn.load_state_dict(torch.load('data/Prior.ckpt'))
Agent.rnn.load_state_dict(torch.load(restore_agent_from))
def pretrain(restore_from=None,
save_to="data/Prior.ckpt",
data="data/mols_filtered.smi",
voc_file="data/Voc",
batch_size=128,
learning_rate=0.001,
n_epochs=5,
store_loss_dir=None,
embedding_size=32):
"""Trains the Prior RNN"""
# Read vocabulary from a file
voc = Vocabulary(init_from_file=voc_file)
# Create a Dataset from a SMILES file
moldata = MolData(data, voc)
data = DataLoader(moldata,
batch_size=batch_size,
shuffle=True,
drop_last=True,
collate_fn=MolData.collate_fn)
Prior = RNN(voc, embedding_size)
# Adding a file to log loss info
if store_loss_dir is None:
out_f = open("loss.csv", "w")
else:
out_f = open("{}/loss.csv".format(store_loss_dir.rstrip("/")), "w")
out_f.write("Step,Loss\n")
# Can restore from a saved RNN
if restore_from:
Prior.rnn.load_state_dict(torch.load(restore_from))
# For later plotting the loss
training_step_counter = 0
n_logging = 100
optimizer = torch.optim.Adam(Prior.rnn.parameters(), lr=learning_rate)
for epoch in range(1, n_epochs + 1):
# When training on a few million compounds, this model converges
# in a few of epochs or even faster. If model sized is increased
# its probably a good idea to check loss against an external set of
# validation SMILES to make sure we dont overfit too much.
for step, batch in tqdm(enumerate(data), total=len(data)):
# Sample from DataLoader
seqs = batch.long()
# Calculate loss
log_p, _ = Prior.likelihood(seqs)
loss = -log_p.mean()
# Calculate gradients and take a step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Logging the loss to a file
if training_step_counter % n_logging == 0:
out_f.write("{},{}\n".format(step, loss))
training_step_counter += 1
# Every 500 steps we decrease learning rate and print some information
if step % 500 == 0 and step != 0:
decrease_learning_rate(optimizer, decrease_by=0.03)
tqdm.write("*" * 50)
tqdm.write(
"Epoch {:3d} step {:3d} loss: {:5.2f}\n".format(
epoch, step, loss.data))
seqs, likelihood, _ = Prior.sample(128)
valid = 0
for i, seq in enumerate(seqs.cpu().numpy()):
smile = voc.decode(seq)
if Chem.MolFromSmiles(smile):
valid += 1
if i < 5:
tqdm.write(smile)
tqdm.write("\n{:>4.1f}% valid SMILES".format(100 * valid /
len(seqs)))
tqdm.write("*" * 50 + "\n")
torch.save(Prior.rnn.state_dict(), save_to)
# Save the Prior
torch.save(Prior.rnn.state_dict(), save_to)
f_out.close()
copyfile('train_agent.py', os.path.join(save_dir, "train_agent.py"))
experience.print_memory(os.path.join(save_dir, "memory"))
torch.save(Agent.rnn.state_dict(), os.path.join(save_dir, 'Agent.ckpt'))
seqs, agent_likelihood, entropy = Agent.sample(batch_size, real_vecs)
prior_likelihood, _ = Prior.likelihood(Variable(seqs))
prior_likelihood = prior_likelihood.data.cpu().numpy()
smiles = seq_to_smiles(seqs, voc)
score = scoring_function(smiles)
with open(os.path.join(save_dir, "sampled"), 'w') as f:
f.write("SMILES Score PriorLogP\n")
for smiles, score, prior_likelihood in zip(smiles, score, prior_likelihood):
f.write("{} {:5.2f} {:6.2f}\n".format(smiles, score, prior_likelihood))
if __name__ == "__main__":
voc = Vocabulary(init_from_file="data/Voc")
# Create a Dataset from a SMILES file
if path.isfile('./data/agent/vecs.dat'):
print('Found vectors, reading from file...')
data = Dataset(voc, "data/mols.smi", vec_file='./data/agent/vecs.dat')
else:
raise ValueError('No agent Data')
mew = data.mew
std = data.std
train_agent(data=data, voc=voc, scoring_function_kwargs={'mew':mew, 'std':std})
def fit(voc_path, mol_path, restore_path, max_save_path, last_save_path,
epoch_num, step_num, decay_step_num, smile_num, lr, weigth_decay):
restore_from = restore_path # if not restore model print None
# Read vocabulary from a file
voc = Vocabulary(init_from_file=voc_path)
# Create a Dataset from a SMILES file
moldata = MolData(mol_path, voc)
data = DataLoader(moldata,
batch_size=128,
shuffle=True,
drop_last=True,
collate_fn=MolData.collate_fn)
Prior = RNN(voc)
# Can restore from a saved RNN
if restore_from:
Prior.rnn.load_state_dict(torch.load(restore_from))
total_loss = []
total_valid = []
max_valid_pro = 0
optimizer = torch.optim.Adam(Prior.rnn.parameters(), lr=lr)
for epoch in range(1, epoch_num):
for step, batch in tqdm(enumerate(data), total=len(data)):
# Sample from DataLoader
seqs = batch.long()
# Calculate loss
log_p, _ = Prior.likelihood(seqs)
loss = -log_p.mean()
# Calculate gradients and take a step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Every 300 steps we decrease learning rate and print some information
if step != 0 and step % decay_step_num == 0:
decrease_learning_rate(optimizer, decrease_by=weigth_decay)
if step % step_num == 0:
tqdm.write("*" * 50)
tqdm.write(
"Epoch {:3d} step {:3d} loss: {:5.2f}\n".format(
epoch, step, loss))
# print("Epoch {:3d} step {:3d} loss: {:5.2f}\n".format(epoch, step, loss))
total_loss.append(float(loss))
seqs, likelihood, _ = Prior.sample(128)
valid = 0
# smiles=[]
# vali_smi=[]
for i, seq in enumerate(seqs.cpu().numpy()):
smile = voc.decode(seq)
# smiles.append(smile)
if Chem.MolFromSmiles(smile):
valid += 1
# vali_smi.append(smile)
if i < smile_num:
print(smile)
vali_pro = valid / len(seqs)
total_valid.append(float(vali_pro))
tqdm.write("\n{:>4.1f}% valid SMILES".format(100 * valid /
len(seqs)))
tqdm.write("*" * 50 + "\n")
if vali_pro > max_valid_pro:
max_valid_pro = vali_pro
torch.save(Prior.rnn.state_dict(), max_save_path)
# Save the Prior
torch.save(Prior.rnn.state_dict(), last_save_path)
print("total loss:", total_loss)
print("total valid:", total_valid)
return total_loss, total_valid
