def main(config):
set_seed(config.seed)
train = read_smiles_csv(config.train_load)
vocab = CharVocab.from_data(train)
torch.save(vocab, config.vocab_save)
torch.save(config, config.config_save)
device = torch.device(config.device)
# condition mode
if config.conditional:
fps = read_fps_csv(config.train_load)
fps = fps_to_list(fps)
fps = [torch.tensor(f, dtype=torch.float, device=device) for f in fps]
# fingerprints length
fps_len = len(fps[0])
else:
fps = None
fps_len = 0
with Pool(config.n_jobs) as pool:
reward_func = MetricsReward(train, config.n_ref_subsample,
config.rollouts, pool,
config.addition_rewards)
model = ORGAN(vocab, config, fps_len, reward_func)
model = model.to(device)
trainer = ORGANTrainer(config)
trainer.fit(model, train, fps)
torch.save(model.state_dict(), config.model_save)
def main(config):
set_seed(config.seed)
train = read_smiles_csv(config.train_load)
if config.conditional_model:
labels = read_label_csv(config.train_load)
config.labels_size = len(labels[0])
labels = [[int(x) for x in list(l)] for l in labels]
train_data = [(x, y) for (x, y) in zip(train, labels)]
else:
train_data = [(x) for x in train]
shuffle(train_data)
train_data = train_data[:500000]
vocab = CharVocab.from_data(train)
torch.save(config, config.config_save)
torch.save(vocab, config.vocab_save)
device = torch.device(config.device)
model = AAE(vocab, config)
model = model.to(device)
trainer = AAETrainer(config)
trainer.fit(model, train_data)
model.to('cpu')
torch.save(model.state_dict(), config.model_save)
def main(config):
set_seed(config.seed)
train = read_smiles_csv(config.train_load)
vocab = CharVocab.from_data(train)
torch.save(config, config.config_save)
torch.save(vocab, config.vocab_save)
device = torch.device(config.device)
model = AAE(vocab, config)
model = model.to(device)
trainer = AAETrainer(config)
trainer.fit(model, train)
model.to('cpu')
torch.save(model.state_dict(), config.model_save)
def merge_vocab(*args):
"""
helper function to merge multiple vocab objects...helpful for cases that may require the processing of more data than
is able to held in memory or for getting a common vocab to use to merge multiple disjoint datasets, etc..
*args: a list of an arbitrary number of vocab objects
"""
# use this list to filter out 'characters' that we don't need to make the new dataset
ignore_char_list = ["<bos>", "<eos>", "<pad>", "<unk>"]
merged_char_set = set()
for vocab_path in args:
vocab = torch.load(vocab_path)
vocab_chars_set = set(
[x for x in vocab.c2i.keys() if x not in ignore_char_list]
)
merged_char_set.update(vocab_chars_set)
return CharVocab(merged_char_set)
def main(config):
set_seed(config.seed)
train = read_smiles_csv(config.train_load)
vocab = CharVocab.from_data(train)
device = torch.device(config.device)
with Pool(config.n_jobs) as pool:
reward_func = MetricsReward(train, config.n_ref_subsample,
config.rollouts, pool,
config.addition_rewards)
model = ORGAN(vocab, config, reward_func)
model = model.to(device)
trainer = ORGANTrainer(config)
trainer.fit(model, train)
torch.save(model.state_dict(), config.model_save)
torch.save(config, config.config_save)
torch.save(vocab, config.vocab_save)
def compute_vocab(smiles_list, n_jobs=mp.cpu_count()):
"""
simple function that can be used to create a vocabulary for an arbitrary set of smiles strings
smiles_list: list of smiles strings
"""
# extract all unique characters in smiles_list
# char_set = set.union(*[set(x) for x in smiles_list])
with mp.Pool(n_jobs) as pool:
result = list(
tqdm(
pool.imap_unordered(compute_vocab_job, smiles_list),
total=len(smiles_list),
)
)
char_set = set.union(*result)
# create the vocab
vocab = CharVocab(char_set)
return vocab
def get_vocabulary(self, data):
return CharVocab.from_data(data)
def fit(self, dataset):
self.vocab = CharVocab.from_data(dataset)
return self
import gentrl
import torch
from moses.metrics.utils import get_mol
import pandas as pd
import pickle
import moses
from moses.utils import CharVocab
from rdkit import RDLogger
RDLogger.DisableLog('rdApp.*')
# Load vocab
dataset_path = "../data/moses_qed_props.csv.gz"
df = pd.read_csv(dataset_path, compression="gzip")
vocab = CharVocab.from_data(df['SMILES'])
enc = gentrl.RNNEncoder(vocab, latent_size=50)
dec = gentrl.DilConvDecoder(vocab, latent_input_size=50, split_len=100)
model = gentrl.GENTRL(enc, dec, 50 * [('c', 20)], [('c', 20)], beta=0.001)
model.cuda()
torch.cuda.set_device(0)
moses_qed_props_model_path = "../models/moses/"
model.load(moses_qed_props_model_path)
model.cuda()
import random
from rdkit import RDLogger
RDLogger.DisableLog('rdApp.*')
generated = []