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data_processing.py
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data_processing.py
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import os
import dgl
import torch
import pickle
import pysmiles
from collections import defaultdict
import pandas as pd
attribute_names = ['element', 'charge', 'aromatic', 'hcount']
class SmilesDataset(dgl.data.DGLDataset):
def __init__(self, args, role='reactant', feature_encoder=None, raw_graphs=None):
self.args = args
if role=='product' and args.corpus:
self.mode = args.corpus
else:
self.mode = args.mode
self.feature_encoder = feature_encoder
self.raw_graphs = raw_graphs
self.role = role
self.path = f'dataset/{self.args.dataset}/cache/{self.mode}_{role}_graphs.bin'
self.graphs = []
super().__init__(name='Smiles_' + self.mode)
def to_gpu(self):
if torch.cuda.is_available():
print('moving ' + self.mode + ' data to GPU')
self.graphs = [graph.to('cuda:' + str(self.args.device)) for graph in self.graphs]
def save(self):
print(f'saving {self.mode} {self.role} graphs to {self.path}')
dgl.save_graphs(self.path, self.graphs)
def load(self):
print(f'loading {self.mode} {self.role} graphs from {self.path}')
self.graphs = dgl.load_graphs(self.path)[0]
self.to_gpu()
def process(self):
print('transforming ' + self.mode + ' data from networkx graphs to DGL graphs')
for i, graph in enumerate(self.raw_graphs):
if i % 10000 == 0:
print('%dk' % (i // 1000))
# transform networkx graphs to dgl graphs
try:
graph = networkx_to_dgl(graph, self.feature_encoder)
self.graphs.append(graph)
except:
self.graphs.append(self.graphs[-1])
self.to_gpu()
def has_cache(self):
return os.path.exists(self.path)
def __getitem__(self, i):
return self.graphs[i]
def __len__(self):
return len(self.graphs)
def networkx_to_dgl(raw_graph, feature_encoder):
# add edges
src = [s for (s, _) in raw_graph.edges]
dst = [t for (_, t) in raw_graph.edges]
graph = dgl.graph((src, dst), num_nodes=len(raw_graph.nodes))
# add node features
node_features = []
for i in range(len(raw_graph.nodes)):
raw_feature = raw_graph.nodes[i]
numerical_feature = []
for j in attribute_names:
if raw_feature[j] in feature_encoder[j]:
numerical_feature.append(feature_encoder[j][raw_feature[j]])
else:
numerical_feature.append(feature_encoder[j]['unknown'])
node_features.append(numerical_feature)
node_features = torch.tensor(node_features)
graph.ndata['feature'] = node_features
# transform to bi-directed graph with self-loops
graph = dgl.to_bidirected(graph, copy_ndata=True)
graph = dgl.add_self_loop(graph)
return graph
def read_data(dataset, mode, featurize=False):
path = f'dataset/{dataset}/raw/{mode}.csv'
print('preprocessing %s data from %s' % (mode, path))
# saving all possible values of each attribute (only for training data)
all_values = defaultdict(set)
graphs = []
df = pd.read_csv(path)
product_smiles_list = df['product_smiles'].tolist()
reactant_smiles_list = df['reactant_smiles'].tolist()
for idx, (product_smiles, reactant_smiles) in enumerate(zip(product_smiles_list, reactant_smiles_list)):
if int(idx) % 10000 == 0:
print('%dk' % (int(idx) // 1000))
# pysmiles.read_smiles() will raise a ValueError: "The atom [se] is malformatted" on USPTO-479k dataset.
# This is because "Se" is in a aromatic ring, so in USPTO-479k, "Se" is transformed to "se" to satisfy
# SMILES rules. But pysmiles does not treat "se" as a valid atom and raise a ValueError. To handle this
# case, I transform all "se" to "Se" in USPTO-479k.
if '[se]' in reactant_smiles:
reactant_smiles = reactant_smiles.replace('[se]', '[Se]')
if '[se]' in product_smiles:
product_smiles = product_smiles.replace('[se]', '[Se]')
# use pysmiles.read_smiles() to parse SMILES and get graph objects (in networkx format)
reactant_graph = pysmiles.read_smiles(reactant_smiles, zero_order_bonds=False)
product_graph = pysmiles.read_smiles(product_smiles, zero_order_bonds=False)
if mode == 'train' or featurize:
# store all values
for graph in [reactant_graph, product_graph]:
for attr in attribute_names:
for _, value in graph.nodes(data=attr):
all_values[attr].add(value)
graphs.append([reactant_graph, product_graph])
if mode == 'train' or featurize:
return all_values, graphs
else:
return graphs
def read_graphs(dataset, mode, role):
path = f'dataset/{dataset}/raw/{mode}.csv'
print('preprocessing %s data from %s' % (mode, path))
# saving all possible values of each attribute (only for training data)
all_values = defaultdict(set)
graphs = []
df = pd.read_csv(path)
smiles_list = df[f'{role}_smiles'].tolist()
for idx, smiles in enumerate(smiles_list):
if int(idx) % 10000 == 0:
print('%dk' % (int(idx) // 1000))
# pysmiles.read_smiles() will raise a ValueError: "The atom [se] is malformatted" on USPTO-479k dataset.
# This is because "Se" is in a aromatic ring, so in USPTO-479k, "Se" is transformed to "se" to satisfy
# SMILES rules. But pysmiles does not treat "se" as a valid atom and raise a ValueError. To handle this
# case, I transform all "se" to "Se" in USPTO-479k.
if '[se]' in smiles:
smiles = smiles.replace('[se]', '[Se]')
# use pysmiles.read_smiles() to parse SMILES and get graph objects (in networkx format)
graph = pysmiles.read_smiles(smiles, zero_order_bonds=False)
graphs.append(graph)
return graphs
def get_feature_encoder(all_values):
feature_encoder = {}
idx = 0
# key: attribute; values: all possible values of the attribute
for key, values in all_values.items():
feature_encoder[key] = {}
for value in values:
feature_encoder[key][value] = idx
idx += 1
# for each attribute, we add an "unknown" key to handle unknown values during inference
feature_encoder[key]['unknown'] = idx
idx += 1
return feature_encoder
def preprocess(dataset):
print('preprocessing %s dataset' % dataset)
# read all data and get all values for attributes
all_values, train_graphs = read_data(dataset, 'train')
valid_graphs = read_data(dataset, 'valid')
test_graphs = read_data(dataset, 'test')
# get one-hot encoder for attribute values
feature_encoder = get_feature_encoder(all_values)
# save feature encoder to disk
path = f'dataset/{args.dataset}/cache/'
if not os.path.exists(path):
os.mkdir(path)
path = 'dataset/' + dataset + '/cache/feature_encoder.pkl'
print('saving feature encoder to %s' % path)
with open(path, 'wb') as f:
pickle.dump(feature_encoder, f)
return feature_encoder, train_graphs, valid_graphs, test_graphs
def preprocess_subset(dataset, mode):
print('preprocessing %s dataset' % dataset)
path = f'dataset/{dataset}/cache/feature_encoder.pkl'
with open(path, 'rb') as f:
feature_encoder = pickle.load(f)
all_values, graphs = read_data(dataset, mode, True)
# feature_encoder = get_feature_encoder(all_values)
# path = f'dataset/{dataset}/cache/feature_encoder.pkl'
# print('saving feature encoder to %s' % path)
# with open(path, 'wb') as f:
# pickle.dump(feature_encoder, f)
return feature_encoder, graphs
def load_data(args):
path = 'dataset/' + args.dataset + '/cache/feature_encoder.pkl'
print('loading feature encoder from %s' % path)
with open(path, 'rb') as f:
feature_encoder = pickle.load(f)
# preprocess reactant graphs
reactant_path = f'dataset/{args.dataset}/cache/{args.mode}_reactant_graphs.bin'
if os.path.exists(reactant_path):
reactant_dataset = SmilesDataset(args)
else:
path = f'dataset/{args.dataset}/cache/'
if not os.path.exists(path):
os.mkdir(path)
graphs = read_graphs(args.dataset, args.mode, 'reactant')
reactant_dataset = SmilesDataset(args, 'reactant', feature_encoder, graphs)
# preprocess product graphs
if args.corpus:
product_path = f'dataset/{args.dataset}/cache/{args.corpus}_product_graphs.bin'
else:
product_path = f'dataset/{args.dataset}/cache/{args.mode}_product_graphs.bin'
if os.path.exists(product_path):
product_dataset = SmilesDataset(args, 'product')
else:
path = f'dataset/{args.dataset}/cache/'
if not os.path.exists(path):
os.mkdir(path)
graphs = read_graphs(args.dataset, args.corpus, 'product')
product_dataset = SmilesDataset(args, 'product', feature_encoder, graphs)
return feature_encoder, (reactant_dataset, product_dataset)
def load_data_with_smiles(args):
data = load_data(args)
df = pd.read_csv(f'dataset/{args.dataset}/raw/{args.mode}.csv')
smiles = [df['reactant_smiles'], df['product_smiles']]
return data, smiles
def load_identifiers(args):
df = pd.read_csv(f'dataset/{args.dataset}/raw/{args.mode}.csv')
reactant_identifiers = {
'smiles': df['reactant_smiles'].tolist(),
'iupac':df['reactant_iupac'].tolist()
}
product_identifiers = {
'smiles': df['product_smiles'].tolist(),
'iupac':df['product_iupac'].tolist()
}
if args.corpus:
df = pd.read_csv(f'dataset/{args.dataset}/raw/{args.corpus}.csv')
corpus_identifiers = {
'smiles': df['product_smiles'].tolist(),
'iupac':df['product_iupac'].tolist()
}
identifiers = {
'reactant': reactant_identifiers,
'product': product_identifiers,
'corpus': corpus_identifiers
}
return identifiers