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grammar_generation.py
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grammar_generation.py
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from rdkit import Chem
from functools import partial
from multiprocessing import Pool
from fuseprop import find_clusters, extract_subgraph, get_mol, get_smiles, find_fragments
from copy import deepcopy
import numpy as np
import torch
import argparse
from private import *
from agent import sample
def data_processing(input_smiles, GNN_model_path, motif=False):
input_mols = []
input_graphs = []
init_subgraphs = []
subgraphs_idx = []
input_graphs_dict = {}
init_edge_flag = 0
for n, smiles in enumerate(input_smiles):
print("data processing {}/{}".format(n, len(input_smiles)))
# Kekulized
smiles = get_smiles(get_mol(smiles))
mol = get_mol(smiles)
input_mols.append(mol)
if not motif:
clusters, atom_cls = find_clusters(mol)
for i,cls in enumerate(clusters):
clusters[i] = set(list(cls))
for a in range(len(atom_cls)):
atom_cls[a] = set(atom_cls[a])
else:
fragments = find_fragments(mol)
clusters = [frag[1] for frag in fragments]
# Construct graphs
subgraphs = []
subgraphs_idx_i = []
for i,cluster in enumerate(clusters):
_, subgraph_i_mapped, _ = extract_subgraph(smiles, cluster)
subgraphs.append(SubGraph(subgraph_i_mapped, mapping_to_input_mol=subgraph_i_mapped, subfrags=list(cluster)))
subgraphs_idx_i.append(list(cluster))
init_edge_flag += 1
init_subgraphs.append(subgraphs)
subgraphs_idx.append(subgraphs_idx_i)
graph = InputGraph(mol, smiles, subgraphs, subgraphs_idx_i, GNN_model_path)
input_graphs.append(graph)
input_graphs_dict[MolKey(graph.mol)] = graph
# Construct subgraph_set
subgraph_set = SubGraphSet(init_subgraphs, subgraphs_idx, input_graphs)
return subgraph_set, input_graphs_dict
def grammar_generation(agent, input_graphs_dict, subgraph_set, grammar, mcmc_iter, sample_number, args):
# Selected hyperedge (subgraph)
plist = [*subgraph_set.map_to_input]
# Terminating condition
if len(plist) == 0:
# done_flag, new_input_graphs_dict, new_subgraph_set, new_grammar
return True, input_graphs_dict, subgraph_set, grammar
# Update every InputGraph: remove every subgraph that equals to p_star, for those subgraphs that contain atom idx in p_star, replace the atom with p_star
org_input_graphs_dict = deepcopy(input_graphs_dict)
org_subgraph_set = deepcopy(subgraph_set)
org_grammar = deepcopy(grammar)
input_graphs_dict = deepcopy(org_input_graphs_dict)
subgraph_set = deepcopy(org_subgraph_set)
grammar = deepcopy(org_grammar)
for i, (key, input_g) in enumerate(input_graphs_dict.items()):
print("---for graph {}---".format(i))
action_list = []
all_final_features = []
# Skip the final iteration for training agent
if len(input_g.subgraphs) > 1:
for subgraph, subgraph_idx in zip(input_g.subgraphs, input_g.subgraphs_idx):
subg_feature = input_g.get_subg_feature_for_agent(subgraph)
num_occurance = subgraph_set.map_to_input[MolKey(subgraph)][1]
num_in_input = len(subgraph_set.map_to_input[MolKey(subgraph)][0].keys())
final_feature = []
final_feature.extend(subg_feature.tolist())
final_feature.append(1 - np.exp(-num_occurance))
final_feature.append(num_in_input / len(list(input_graphs_dict.keys())))
all_final_features.append(torch.unsqueeze(torch.from_numpy(np.array(final_feature)).float(), 0))
while(True):
action_list, take_action = sample(agent, torch.vstack(all_final_features), mcmc_iter, sample_number)
if take_action:
break
elif len(input_g.subgraphs) == 1:
action_list = [1]
else:
continue
print("Hyperedge sampling:", action_list)
# Merge connected hyperedges
p_star_list = input_g.merge_selected_subgraphs(action_list)
# Generate rules
for p_star in p_star_list:
is_inside, subgraphs, subgraphs_idx = input_g.is_candidate_subgraph(p_star)
if is_inside:
for subg, subg_idx in zip(subgraphs, subgraphs_idx):
if subg_idx not in input_g.subgraphs_idx:
# Skip the subg if it has been merged in previous iterations
continue
grammar = generate_rule(input_g, subg, grammar)
input_g.update_subgraph(subg_idx)
# Update subgraph_set
subgraph_set.update([g for (k, g) in input_graphs_dict.items()])
new_grammar = deepcopy(grammar)
new_input_graphs_dict = deepcopy(input_graphs_dict)
new_subgraph_set = deepcopy(subgraph_set)
return False, new_input_graphs_dict, new_subgraph_set, new_grammar
def MCMC_sampling(agent, all_input_graphs_dict, all_subgraph_set, all_grammar, sample_number, args):
iter_num = 0
while(True):
print("======MCMC iter{}======".format(iter_num))
done_flag, new_input_graphs_dict, new_subgraph_set, new_grammar = grammar_generation(agent, all_input_graphs_dict, all_subgraph_set, all_grammar, iter_num, sample_number, args)
print("Graph contraction status: ", done_flag)
if done_flag:
break
all_input_graphs_dict = deepcopy(new_input_graphs_dict)
all_subgraph_set = deepcopy(new_subgraph_set)
all_grammar = deepcopy(new_grammar)
iter_num += 1
return iter_num, new_grammar, new_input_graphs_dict
def random_produce(grammar):
def sample(l, prob=None):
if prob is None:
prob = [1/len(l)] * len(l)
idx = np.random.choice(range(len(l)), 1, p=prob)[0]
return l[idx], idx
def prob_schedule(_iter, selected_idx):
prob_list = []
# prob = exp(a * t * x), x = {0, 1}
a = 0.5
for rule_i, rule in enumerate(grammar.prod_rule_list):
x = rule.is_ending
if rule.is_start_rule:
prob_list.append(0)
else:
prob_list.append(np.exp(a * _iter * x))
prob_list = np.array(prob_list)[selected_idx]
prob_list = prob_list / np.sum(prob_list)
return prob_list
hypergraph = Hypergraph()
starting_rules = [(rule_i, rule) for rule_i, rule in enumerate(grammar.prod_rule_list) if rule.is_start_rule]
iter = 0
while(True):
if iter == 0:
_, idx = sample(starting_rules)
selected_rule_idx, selected_rule = starting_rules[idx]
hg_cand, _, avail = selected_rule.graph_rule_applied_to(hypergraph)
hypergraph = deepcopy(hg_cand)
else:
candidate_rule = []
candidate_rule_idx = []
candidate_hg = []
for rule_i, rule in enumerate(grammar.prod_rule_list):
hg_prev = deepcopy(hypergraph)
hg_cand, _, avail = rule.graph_rule_applied_to(hypergraph)
if(avail):
candidate_rule.append(rule)
candidate_rule_idx.append(rule_i)
candidate_hg.append(hg_cand)
if (all([rl.is_start_rule for rl in candidate_rule]) and iter > 0) or iter > 30:
break
prob_list = prob_schedule(iter, candidate_rule_idx)
hypergraph, idx = sample(candidate_hg, prob_list)
selected_rule = candidate_rule_idx[idx]
iter += 1
try:
mol = hg_to_mol(hypergraph)
print(Chem.MolToSmiles(mol))
except:
return None, iter
return mol, iter