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graph_generator.py
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graph_generator.py
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""" Graph Generator """
import pandas as pd
import torch
import sys
import dgl
import os
import networkx as nx
from rdkit import Chem
import pickle
import shutil
import zipfile
import arguments
from arguments import args
from utils_generator import create_descriptors, identify_functional_groups, \
CanonicalAtomFeaturizer, CanonicalBondFeaturizer, \
RandomScaffoldSplitter, RandomSplitter, RandomStratifiedSplitter_m,\
DatasetTox21, DatasetBBBP, DatasetBace, DatasetToxcast, \
DatasetClintox, DatasetSider, DatasetLipo, DatasetESOL, DatasetFreeSolv,\
fg_idx, fg_types_idx, convert_mol, make_df, fgs_connections_idx, \
graph_constructor, to_networkx, quotient_generator, splitted_data
current_dir = args.current_dir
dataset_names = {
"tox21" : "DatasetTox21",
"bbbp" : "DatasetBBBP",
"bace" : "DatasetBace",
"toxcast" : "DatasetToxcast",
"clintox" : "DatasetClintox",
"sider" : "DatasetSider",
"lipo": "DatasetLipo",
"esol" : "DatasetESOL",
"freesolv" : "DatasetFreeSolv",
}
# generating global features
for name in args.gen_names_data:
name_global_csv = name + '_global_cdf_rdkit.csv'
name_global_zip = name + '_global_cdf_rdkit.zip'
saving_address = current_dir + 'data/global_features/' + name_global_zip
if not os.path.exists(saving_address):
print('Generating Global Features for', name)
raw_data_url = current_dir + 'data/raw/' + name + '.csv'
data = pd.read_csv(raw_data_url)
descriptors = create_descriptors(data, mols_column_name='smiles')
compression_opts = dict(method='zip', archive_name=name_global_csv)
os.makedirs(os.path.dirname(saving_address), exist_ok=True)
descriptors.to_csv(saving_address, index=False, compression=compression_opts)
# generating node features (for faster graph generation)
atom_featurizer = CanonicalAtomFeaturizer()
bond_featurizer = CanonicalBondFeaturizer()
for name in args.gen_names_data:
saving_address = current_dir + 'data/node_features/' + name + '_node_127_one_hot' + '.zip'
saving_address_pickle = current_dir + 'data/node_features/' + 'node_features.pickle'
if not os.path.exists(saving_address):
print('Generating Node Features for', name)
raw_data_url = current_dir + 'data/raw/' + name + '.csv'
data = pd.read_csv(raw_data_url)
node_features=[]
for i in range(len(data.smiles)):
node_features.append(atom_featurizer(Chem.MolFromSmiles(data.smiles[i]))['h'])
os.makedirs(os.path.dirname(saving_address), exist_ok=True)
with open(saving_address_pickle , 'wb') as handle:
pickle.dump(node_features, handle)
zf = zipfile.ZipFile(saving_address, 'w', zipfile.ZIP_DEFLATED)
zf.write(saving_address_pickle, 'node_features.pickle') #archname is necessary to remove the path once unpacked
zf.close()
os.remove(saving_address_pickle)
# generating splits
scaffold_splitter = RandomScaffoldSplitter()
random_splitter = RandomSplitter()
stratified_splitter = RandomStratifiedSplitter_m()
splitters = [scaffold_splitter, random_splitter, stratified_splitter]
type_indexs = {'scaffold' : 0, 'random' : 1, 'stratified' : 2}
# splitting
for name in args.gen_names_data:
raw_data_url = current_dir + 'data/raw/' + name + '.csv'
data = pd.read_csv(raw_data_url)
for split in args.splits:
for seed in args.generation_seeds:
saving_address = current_dir + 'data/splits/' + name + '/' + split + '_' + str(seed) + '/'
if not os.path.exists(saving_address + 'train_smiles') or not os.path.exists(saving_address + 'val_smiles') or not os.path.exists(saving_address + 'test_smiles'):
print('Generating', split, 'seed_', seed, 'split for', name)
splitted_sets = splitters[type_indexs[split]].split(data, seed=seed)
smiles_train = [data.smiles[i] for i in splitted_sets[0]]
smiles_val = [data.smiles[i] for i in splitted_sets[1]]
smiles_test = [data.smiles[i] for i in splitted_sets[2]]
os.makedirs(os.path.dirname(saving_address), exist_ok=True)
with open(saving_address + 'train_smiles', 'wb') as handle:
pickle.dump(smiles_train, handle, protocol=pickle.HIGHEST_PROTOCOL)
with open(saving_address + 'val_smiles', 'wb') as handle:
pickle.dump(smiles_val, handle, protocol=pickle.HIGHEST_PROTOCOL)
with open(saving_address + 'test_smiles', 'wb') as handle:
pickle.dump(smiles_test, handle, protocol=pickle.HIGHEST_PROTOCOL)
#generating graphs
for name_data in args.gen_names_data:
for name_model in arguments.generation_models_rows.keys():
for idx_row_graph_gen in arguments.generation_models_rows[name_model]:
## using generated_featurized_graphs for all seeds
generated_featurized_graphs = {}
are_graphs_generated = False
# specific settings for this scenario
dataset_name = dataset_names[name]
# """ Load csv of the specific graph generator"""
# save_csv = current_dir + "data/graph/"
# graph_gen_csv = pd.read_csv(save_csv + name_model+".csv")
# row_graph_gen = graph_gen_csv.iloc[idx_row_graph_gen]
if args.HQ_first_aggregation_op == 'sum':
print('Caution: Quotient by non-FGs with sum aggregation')
for split in args.splits:
print( '\n\n ========================= \n Generating', name_model, 'with row number', idx_row_graph_gen, '. Generating graphs', \
'for', split, 'splitting with seeds', args.generation_seeds, 'for',\
name_data, 'dataset is started! \n ========================= \n\n')
### Generation
# incorrect = False
list_gen_seeds = []
for seed in args.generation_seeds:
path_save= current_dir+"data/graph/"+name_data+"/"+split+"_"+str(seed)+"/"+arguments.name_final_zip
if not os.path.exists(path_save):
save_results_status = True
list_gen_seeds.append(seed)
# Print the current seed
print("Seed ", seed, " is started!")
name_node_feats_zip = name_data+ "_node"+arguments.name_node_feature+".zip"
name_global_csv = name_data+"_global_cdf_rdkit.csv"
name_global_zip = name_data+"_global_cdf_rdkit.zip"
"""Set Path"""
folder_data_temp = current_dir +"data/buffer/"
path_global_csv = folder_data_temp + name_global_csv
path_save_current_dir = folder_data_temp + arguments.name_final + "/"
path_save_temp = path_save_current_dir + split + "_" + str(seed)
path_save_0 = current_dir+"data/graph/"+name_data
path_save = current_dir+"data/graph/"+name_data+"/"+split+"_"+str(seed)+"/"
path_node_feats = current_dir + 'data/node_features/'
path_node_feats_zip = path_node_feats + name_node_feats_zip
path_smiles = current_dir + 'data/splits/' + name_data + "/" + split+"_"+str(seed)+"/"
path_data_csv = current_dir + 'data/raw/' + name_data + ".csv"
path_global_zip = current_dir + 'data/global_features/' + name_global_zip
#dataframe containing some details about the molecule
df = make_df(path_data_csv)
"""Hierarchical_Quotient"""
if name_model == "Hierarchical_Quotient":
def add_features(smiles, mol_dgl_graph):
# global incorrect
mol = Chem.MolFromSmiles(smiles)
# Add edge features
try:
mol_dgl_graph.edata["e"] = bond_featurizer(mol)["e"]
except:
# pass
mol_dgl_graph.edata["e"] = torch.zeros(mol_dgl_graph.num_edges(), 12)
### Adding a new column
mol_dgl_graph.edata["e"]=torch.cat((mol_dgl_graph.edata["e"], torch.ones(mol_dgl_graph.num_edges()).view(-1,1)), 1)
# Add node features
smiles_idx = dataset_smiles_series[dataset_smiles_series==smiles].index.values[0]
mol_dgl_graph.ndata["v"] = Node_features_loaded[smiles_idx]
### Adding a new column
mol_dgl_graph.ndata["v"]=torch.cat((mol_dgl_graph.ndata["v"], torch.ones(mol_dgl_graph.num_nodes()).view(-1,1)), 1)
#Quotient based on Carbons, FGs
mol_dgl_graph_q0 = quotient_generator(mol_dgl_graph, edge_condition_feature="edges_non_fgs", op=args.HQ_first_aggregation_op,\
another_edges_feature="edges_fgs").int()
mol_dgl_graph_q1 = quotient_generator(mol_dgl_graph_q0, edge_condition_feature="edges_fgs", op="sum").int()
mol_dgl_graph.ndata["qn2"]=torch.full((mol_dgl_graph.num_nodes(),1), 0).to(torch.int32)
mol_dgl_graph.edata["qe2"]=torch.full((mol_dgl_graph.num_edges(),1), 0).to(torch.float32)
####
mol_dgl_graph_q1.ndata["qn1"]=torch.full((mol_dgl_graph_q1.num_nodes(),1), -1).to(torch.int32)
mol_dgl_graph_q1.edata["qe1"]=torch.full((mol_dgl_graph_q1.num_edges(),1), -1).to(torch.float32)
####
mol_dgl_graph_q1.ndata["qn2"]= mol_dgl_graph_q1.ndata["qn2"].to(dtype=torch.int32)
mol_dgl_graph.ndata["qn1"]= mol_dgl_graph.ndata["qn1"].to(dtype=torch.int32)
mol_dgl_graph_q1.ndata["v"][:, -1] = 2
mol_dgl_graph_q1.edata["e"][:, -1] = 2
mol_dgl_graph_q1.ndata.pop('qn1')
mol_dgl_graph_q1.ndata.pop('qn2')
mol_dgl_graph_q1.edata.pop('qe1')
mol_dgl_graph_q1.edata.pop('qe2')
return mol_dgl_graph_q1
"""Replace global features NaN values with median (before making dataset)"""
if not os.path.exists(current_dir+name_global_csv):
shutil.unpack_archive(path_global_zip, folder_data_temp)
glob_csv = pd.read_csv(path_global_csv)
list_nan = []
for i in range(glob_csv.shape[1]):
if glob_csv.iloc[:,i].isnull().sum()>0:
list_nan.append(i)
print("list_nan_global_before: ", list_nan)
for i in list_nan:
glob_csv.iloc[:,i].fillna(glob_csv.iloc[:,i].median(), inplace=True)
list_nan = []
for i in range(glob_csv.shape[1]):
if glob_csv.iloc[:,i].isnull().sum()>0:
list_nan.append(i)
print("list_nan_global_after: ", list_nan)
glob_csv.to_csv(path_global_csv, index = False)
"""Load node features"""
if not os.path.exists(current_dir+"node_features.pickle"):
shutil.unpack_archive(path_node_feats_zip, folder_data_temp)
with open(folder_data_temp+"node_features.pickle", "rb") as handle:
Node_features_loaded = pickle.load(handle)
"""Make dataset"""
dataset = eval(dataset_name)(path_data_csv, path_global_csv)
# Define a pandas series from dataset smiles
dataset_smiles_series = dataset.smiles.squeeze()
# Train, validation, and test set split
train_set = splitted_data(path_smiles, dataset, dataset_smiles_series, string="train")
val_set = splitted_data(path_smiles, dataset, dataset_smiles_series, string="val")
test_set = splitted_data(path_smiles, dataset, dataset_smiles_series, string="test")
print(len(train_set), len(val_set), len(test_set))
"""Check if parameters of generator model are True or not"""
g = add_features(train_set[0][0], graph_constructor(df, train_set[0][0]))
# if incorrect:
# sys.exit("Seed "+str(seed)+" is exited, because the model settings are incorrect!")
# else:
# print("The model settings are correct!")
# nx.draw_networkx(g.to_networkx(), with_labels = True)
"""Prepare Training, Validation, and Test Sets"""
shutil.rmtree(folder_data_temp, ignore_errors=True)
#improving generation's speed with generating graphs only once for all seeds
#and splits in a (graph_model,row ID)
if not are_graphs_generated:
print('== \n Generating Featurized Graphs! \n ==')
for i in dataset_smiles_series:
generated_featurized_graphs[i] = add_features(i, graph_constructor(df, i))
are_graphs_generated = True
print(g, generated_featurized_graphs[train_set[0][0]])
"""Training Set"""
dgl_train=[]
smiles_train = []
labels_train= torch.empty(0)
masks_train = torch.empty(0)
globals_train = torch.empty(0)
counter = 0
for member in train_set:
bg = generated_featurized_graphs[member[0]]
dgl_train.append(bg)
smiles_train.append(member[0])
labels_train= torch.cat((labels_train, member[1]), dim=0)
masks_train= torch.cat((masks_train, member[2]), dim=0)
globals_train= torch.cat((globals_train, member[3]), dim=0)
counter+=1
print(counter)
label={"labels":labels_train, "masks":masks_train, "globals":globals_train}
new_path=path_save_temp+"_train.bin"
dgl.save_graphs(new_path, dgl_train, labels=label)
new_path=path_save_temp+"_smiles_train.pickle"
pickle_out = open(new_path,"wb")
pickle.dump(smiles_train, pickle_out)
pickle_out.close()
"""Validtion Set"""
dgl_val=[]
smiles_val = []
labels_val= torch.empty(0)
masks_val = torch.empty(0)
globals_val = torch.empty(0)
counter = 0
for member in val_set:
bg = generated_featurized_graphs[member[0]]
dgl_val.append(bg)
smiles_val.append(member[0])
labels_val= torch.cat((labels_val, member[1]), dim=0)
masks_val= torch.cat((masks_val, member[2]), dim=0)
globals_val= torch.cat((globals_val, member[3]), dim=0)
counter+=1
print(counter)
label={"labels":labels_val, "masks":masks_val, "globals":globals_val}
new_path=path_save_temp+"_val.bin"
dgl.save_graphs(new_path, dgl_val, labels=label)
new_path=path_save_temp+"_smiles_val.pickle"
pickle_out = open(new_path,"wb")
pickle.dump(smiles_val, pickle_out)
pickle_out.close()
"""Test Set"""
dgl_test=[]
smiles_test = []
labels_test= torch.empty(0)
masks_test = torch.empty(0)
globals_test = torch.empty(0)
counter = 0
for member in test_set:
bg = generated_featurized_graphs[member[0]]
dgl_test.append(bg)
smiles_test.append(member[0])
labels_test= torch.cat((labels_test, member[1]), dim=0)
masks_test= torch.cat((masks_test, member[2]), dim=0)
globals_test= torch.cat((globals_test, member[3]), dim=0)
counter+=1
print(counter)
label={"labels":labels_test, "masks":masks_test, "globals":globals_test}
new_path=path_save_temp+"_test.bin"
dgl.save_graphs(new_path, dgl_test, labels=label)
new_path=path_save_temp+"_smiles_test.pickle"
pickle_out = open(new_path,"wb")
pickle.dump(smiles_test, pickle_out)
pickle_out.close()
shutil.make_archive(path_save_current_dir, 'zip', path_save_current_dir)
os.makedirs(path_save, exist_ok=True)
b = os.path.join(path_save, arguments.name_final_zip)
shutil.copy(folder_data_temp+arguments.name_final_zip, b)
shutil.rmtree(folder_data_temp, ignore_errors=True)
print("Seed ", seed, " is finished!")
# if incorrect == True:
# os._exit(0)
else:
list_gen_seeds.append(seed)
print("Seed ", seed, " was generated before!")