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simple_main.py
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simple_main.py
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from rdkit import Chem
from copy import deepcopy
import numpy as np
from private import *
from grammar_generation import *
from agent import Agent
import torch.optim as optim
import torch.multiprocessing as mp
import logging
import torch
import math
import os
import time
import pprint
import pickle
import argparse
import fcntl
from retro_star_listener import lock
def evaluate(grammar, args, metrics=['diversity', 'syn']):
# Metric evalution for the given gramamr
div = InternalDiversity()
eval_metrics = {}
generated_samples = []
generated_samples_canonical_sml = []
iter_num_list = []
idx = 0
no_newly_generated_iter = 0
print("Start grammar evaluation...")
while(True):
print("Generating sample {}/{}".format(idx, args.num_generated_samples))
mol, iter_num = random_produce(grammar)
if mol is None:
no_newly_generated_iter += 1
continue
can_sml_mol = Chem.CanonSmiles(Chem.MolToSmiles(mol))
if can_sml_mol not in generated_samples_canonical_sml:
generated_samples.append(mol)
generated_samples_canonical_sml.append(can_sml_mol)
iter_num_list.append(iter_num)
idx += 1
no_newly_generated_iter = 0
else:
no_newly_generated_iter += 1
if idx >= args.num_generated_samples or no_newly_generated_iter > 10:
break
for _metric in metrics:
assert _metric in ['diversity', 'num_rules', 'num_samples', 'syn']
if _metric == 'diversity':
diversity = div.get_diversity(generated_samples)
eval_metrics[_metric] = diversity
elif _metric == 'num_rules':
eval_metrics[_metric] = grammar.num_prod_rule
elif _metric == 'num_samples':
eval_metrics[_metric] = idx
elif _metric == 'syn':
eval_metrics[_metric] = retro_sender(generated_samples, args)
else:
raise NotImplementedError
return eval_metrics
def retro_sender(generated_samples, args):
# File communication to obtain retro-synthesis rate
with open(args.receiver_file, 'w') as fw:
fw.write('')
while(True):
with open(args.sender_file, 'r') as fr:
editable = lock(fr)
if editable:
with open(args.sender_file, 'w') as fw:
for sample in generated_samples:
fw.write('{}\n'.format(Chem.MolToSmiles(sample)))
break
fcntl.flock(fr, fcntl.LOCK_UN)
num_samples = len(generated_samples)
print("Waiting for retro_star evaluation...")
while(True):
with open(args.receiver_file, 'r') as fr:
editable = lock(fr)
if editable:
syn_status = []
lines = fr.readlines()
if len(lines) == num_samples:
for idx, line in enumerate(lines):
splitted_line = line.strip().split()
syn_status.append((idx, splitted_line[2]))
break
fcntl.flock(fr, fcntl.LOCK_UN)
time.sleep(1)
assert len(generated_samples) == len(syn_status)
return np.mean([int(eval(s[1])) for s in syn_status])
def learn(smiles_list, args):
# Create logger
save_log_path = 'log-num_generated_samples{}-{}'.format(args.num_generated_samples, time.strftime("%Y%m%d-%H%M%S"))
create_exp_dir(save_log_path, scripts_to_save=[f for f in os.listdir('./') if f.endswith('.py')])
logger = create_logger('global_logger', save_log_path + '/log.txt')
logger.info('args:{}'.format(pprint.pformat(args)))
logger = logging.getLogger('global_logger')
# Initialize dataset & potential function (agent) & optimizer
subgraph_set_init, input_graphs_dict_init = data_processing(smiles_list, args.GNN_model_path, args.motif)
agent = Agent(feat_dim=300, hidden_size=args.hidden_size)
if args.resume:
assert os.path.exists(args.resume_path), "Please provide valid path for resuming."
ckpt = torch.load(args.resume_path)
agent.load_state_dict(ckpt)
# Start training
logger.info('starting\n')
curr_max_R = 0
for train_epoch in range(args.max_epoches):
returns = []
log_returns = []
logger.info("<<<<< Epoch {}/{} >>>>>>".format(train_epoch, args.max_epoches))
# MCMC sampling
for num in range(args.MCMC_size):
grammar_init = ProductionRuleCorpus()
l_input_graphs_dict = deepcopy(input_graphs_dict_init)
l_subgraph_set = deepcopy(subgraph_set_init)
l_grammar = deepcopy(grammar_init)
iter_num, l_grammar, l_input_graphs_dict = MCMC_sampling(agent, l_input_graphs_dict, l_subgraph_set, l_grammar, num, args)
# Grammar evaluation
eval_metric = evaluate(l_grammar, args, metrics=['diversity', 'syn'])
logger.info("eval_metrics: {}".format(eval_metric))
# Record metrics
R = eval_metric['diversity'] + 2 * eval_metric['syn']
R_ind = R.copy()
returns.append(R)
log_returns.append(eval_metric)
logger.info("======Sample {} returns {}=======:".format(num, R_ind))
# Save ckpt
if R_ind > curr_max_R:
torch.save(agent.state_dict(), os.path.join(save_log_path, 'epoch_agent_{}_{}.pkl'.format(train_epoch, R_ind)))
with open('{}/epoch_grammar_{}_{}.pkl'.format(save_log_path, train_epoch, R_ind), 'wb') as outp:
pickle.dump(l_grammar, outp, pickle.HIGHEST_PROTOCOL)
with open('{}/epoch_input_graphs_{}_{}.pkl'.format(save_log_path, train_epoch, R_ind), 'wb') as outp:
pickle.dump(l_input_graphs_dict, outp, pickle.HIGHEST_PROTOCOL)
curr_max_R = R_ind
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='MCMC training')
parser.add_argument('--training_data', type=str, default="./datasets/isocyanates.txt", help="file name of the training data")
parser.add_argument('--GNN_model_path', type=str, default="./GCN/model_gin/supervised_contextpred.pth", help="file name of the pretrained GNN model")
parser.add_argument('--hidden_size', type=int, default=128, help="hidden size of the potential function")
parser.add_argument('--max_epoches', type=int, default=1, help="maximal training epoches")
parser.add_argument('--num_generated_samples', type=int, default=100, help="number of generated samples to evaluate grammar")
parser.add_argument('--MCMC_size', type=int, default=1, help="sample number of each step of MCMC")
parser.add_argument('--learning_rate', type=int, default=1e-2, help="learning rate")
parser.add_argument('--gammar', type=float, default=0.99, help="discount factor")
parser.add_argument('--motif', action="store_true", default=False, help="use motif as the basic building block for polymer dataset")
parser.add_argument('--sender_file', type=str, default="generated_samples.txt", help="file name of the generated samples")
parser.add_argument('--receiver_file', type=str, default="output_syn.txt", help="file name of the output file of Retro*")
parser.add_argument('--resume', action="store_true", default=False, help="resume model")
parser.add_argument('--resume_path', type=str, default='', help="resume path")
args = parser.parse_args()
# Get raw training data
assert os.path.exists(args.training_data), "Please provide valid path of training data."
# Remove duplicated molecules
with open(args.training_data, 'r') as fr:
lines = fr.readlines()
mol_sml = []
for line in lines:
if not (line.strip() in mol_sml):
mol_sml.append(line.strip())
# Clear the communication files for Retro*
with open(args.sender_file, 'w') as fw:
fw.write('')
with open(args.receiver_file, 'w') as fw:
fw.write('')
# Grammar learning
learn(mol_sml, args)