381 lines
16 KiB
Python
381 lines
16 KiB
Python
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import sys
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sys.path.append('../')
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import os
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import os.path as osp
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import pathlib
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import json
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import torch
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import torch.nn.functional as F
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from rdkit import Chem, RDLogger
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from rdkit.Chem.rdchem import BondType as BT
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from tqdm import tqdm
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import numpy as np
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import pandas as pd
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from torch_geometric.data import Data, InMemoryDataset
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from torch_geometric.loader import DataLoader
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from sklearn.model_selection import train_test_split
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import utils as utils
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from datasets.abstract_dataset import AbstractDatasetInfos, AbstractDataModule
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from diffusion.distributions import DistributionNodes
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bonds = {BT.SINGLE: 1, BT.DOUBLE: 2, BT.TRIPLE: 3, BT.AROMATIC: 4}
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class DataModule(AbstractDataModule):
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def __init__(self, cfg):
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self.datadir = cfg.dataset.datadir
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self.task = cfg.dataset.task_name
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super().__init__(cfg)
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def prepare_data(self) -> None:
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target = getattr(self.cfg.dataset, 'guidance_target', None)
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base_path = pathlib.Path(os.path.realpath(__file__)).parents[2]
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root_path = os.path.join(base_path, self.datadir)
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self.root_path = root_path
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batch_size = self.cfg.train.batch_size
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num_workers = self.cfg.train.num_workers
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pin_memory = self.cfg.dataset.pin_memory
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dataset = Dataset(source=self.task, root=root_path, target_prop=target, transform=None)
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if len(self.task.split('-')) == 2:
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train_index, val_index, test_index, unlabeled_index = self.fixed_split(dataset)
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else:
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train_index, val_index, test_index, unlabeled_index = self.random_data_split(dataset)
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self.train_index, self.val_index, self.test_index, self.unlabeled_index = train_index, val_index, test_index, unlabeled_index
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train_index, val_index, test_index, unlabeled_index = torch.LongTensor(train_index), torch.LongTensor(val_index), torch.LongTensor(test_index), torch.LongTensor(unlabeled_index)
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if len(unlabeled_index) > 0:
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train_index = torch.cat([train_index, unlabeled_index], dim=0)
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train_dataset, val_dataset, test_dataset = dataset[train_index], dataset[val_index], dataset[test_index]
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self.train_dataset = train_dataset
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self.train_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True, pin_memory=pin_memory)
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self.val_loader = DataLoader(val_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False, pin_memory=False)
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self.test_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False, pin_memory=False)
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training_iterations = len(train_dataset) // batch_size
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self.training_iterations = training_iterations
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def random_data_split(self, dataset):
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nan_count = torch.isnan(dataset.y[:, 0]).sum().item()
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labeled_len = len(dataset) - nan_count
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full_idx = list(range(labeled_len))
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train_ratio, valid_ratio, test_ratio = 0.6, 0.2, 0.2
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train_index, test_index, _, _ = train_test_split(full_idx, full_idx, test_size=test_ratio, random_state=42)
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train_index, val_index, _, _ = train_test_split(train_index, train_index, test_size=valid_ratio/(valid_ratio+train_ratio), random_state=42)
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unlabeled_index = list(range(labeled_len, len(dataset)))
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print(self.task, ' dataset len', len(dataset), 'train len', len(train_index), 'val len', len(val_index), 'test len', len(test_index), 'unlabeled len', len(unlabeled_index))
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return train_index, val_index, test_index, unlabeled_index
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def fixed_split(self, dataset):
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if self.task == 'O2-N2':
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test_index = [42,43,92,122,197,198,251,254,257,355,511,512,549,602,603,604]
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else:
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raise ValueError('Invalid task name: {}'.format(self.task))
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full_idx = list(range(len(dataset)))
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full_idx = list(set(full_idx) - set(test_index))
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train_ratio = 0.8
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train_index, val_index, _, _ = train_test_split(full_idx, full_idx, test_size=1-train_ratio, random_state=42)
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print(self.task, ' dataset len', len(dataset), 'train len', len(train_index), 'val len', len(val_index), 'test len', len(test_index))
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return train_index, val_index, test_index, []
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def get_train_smiles(self):
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filename = f'{self.task}.csv.gz'
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df = pd.read_csv(f'{self.root_path}/raw/{filename}')
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df_test = df.iloc[self.test_index]
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df = df.iloc[self.train_index]
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smiles_list = df['smiles'].tolist()
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smiles_list_test = df_test['smiles'].tolist()
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smiles_list = [Chem.MolToSmiles(Chem.MolFromSmiles(smi)) for smi in smiles_list]
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smiles_list_test = [Chem.MolToSmiles(Chem.MolFromSmiles(smi)) for smi in smiles_list_test]
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return smiles_list, smiles_list_test
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def get_data_split(self):
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filename = f'{self.task}.csv.gz'
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df = pd.read_csv(f'{self.root_path}/raw/{filename}')
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df_val = df.iloc[self.val_index]
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df_test = df.iloc[self.test_index]
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df_train = df.iloc[self.train_index]
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return df_train, df_val, df_test
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def example_batch(self):
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return next(iter(self.val_loader))
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def train_dataloader(self):
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return self.train_loader
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def val_dataloader(self):
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return self.val_loader
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def test_dataloader(self):
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return self.test_loader
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class Dataset(InMemoryDataset):
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def __init__(self, source, root, target_prop=None,
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transform=None, pre_transform=None, pre_filter=None):
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self.target_prop = target_prop
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self.source = source
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super().__init__(root, transform, pre_transform, pre_filter)
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self.data, self.slices = torch.load(self.processed_paths[0])
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@property
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def raw_file_names(self):
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return [f'{self.source}.csv.gz']
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@property
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def processed_file_names(self):
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return [f'{self.source}.pt']
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def process(self):
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RDLogger.DisableLog('rdApp.*')
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data_path = osp.join(self.raw_dir, self.raw_file_names[0])
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data_df = pd.read_csv(data_path)
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def mol_to_graph(mol, sa, sc, target, target2=None, target3=None, valid_atoms=None):
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type_idx = []
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heavy_atom_indices, active_atoms = [], []
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for atom in mol.GetAtoms():
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if atom.GetAtomicNum() != 1:
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type_idx.append(119-2) if atom.GetSymbol() == '*' else type_idx.append(atom.GetAtomicNum()-2)
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heavy_atom_indices.append(atom.GetIdx())
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active_atoms.append(atom.GetSymbol())
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if valid_atoms is not None:
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if not atom.GetSymbol() in valid_atoms:
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return None, None
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x = torch.LongTensor(type_idx)
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edges_list = []
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edge_type = []
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for bond in mol.GetBonds():
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start, end = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()
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if start in heavy_atom_indices and end in heavy_atom_indices:
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start_new, end_new = heavy_atom_indices.index(start), heavy_atom_indices.index(end)
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edges_list.append((start_new, end_new))
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edge_type.append(bonds[bond.GetBondType()])
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edges_list.append((end_new, start_new))
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edge_type.append(bonds[bond.GetBondType()])
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edge_index = torch.tensor(edges_list, dtype=torch.long).t()
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edge_type = torch.tensor(edge_type, dtype=torch.long)
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edge_attr = edge_type
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if target3 is not None:
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y = torch.tensor([sa, sc, target, target2, target3], dtype=torch.float).view(1,-1)
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elif target2 is not None:
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y = torch.tensor([sa, sc, target, target2], dtype=torch.float).view(1,-1)
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else:
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y = torch.tensor([sa, sc, target], dtype=torch.float).view(1,-1)
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data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i)
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if self.pre_transform is not None:
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data = self.pre_transform(data)
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return data, active_atoms
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# Loop through every row in the DataFrame and apply the function
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data_list = []
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len_data = len(data_df)
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with tqdm(total=len_data) as pbar:
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# --- data processing start ---
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active_atoms = set()
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for i, (sms, df_row) in enumerate(data_df.iterrows()):
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if i == sms:
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sms = df_row['smiles']
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mol = Chem.MolFromSmiles(sms, sanitize=False)
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if len(self.target_prop.split('-')) == 2:
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target1, target2 = self.target_prop.split('-')
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data, cur_active_atoms = mol_to_graph(mol, df_row['SA'], df_row['SC'], df_row[target1], target2=df_row[target2])
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elif len(self.target_prop.split('-')) == 3:
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target1, target2, target3 = self.target_prop.split('-')
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data, cur_active_atoms = mol_to_graph(mol, df_row['SA'], df_row['SC'], df_row[target1], target2=df_row[target2], target3=df_row[target3])
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else:
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data, cur_active_atoms = mol_to_graph(mol, df_row['SA'], df_row['SC'], df_row[self.target_prop])
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active_atoms.update(cur_active_atoms)
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data_list.append(data)
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pbar.update(1)
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torch.save(self.collate(data_list), self.processed_paths[0])
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class DataInfos(AbstractDatasetInfos):
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def __init__(self, datamodule, cfg):
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tasktype_dict = {
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'hiv_b': 'classification',
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'bace_b': 'classification',
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'bbbp_b': 'classification',
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'O2': 'regression',
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'N2': 'regression',
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'CO2': 'regression',
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}
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task_name = cfg.dataset.task_name
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self.task = task_name
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self.task_type = tasktype_dict.get(task_name, "regression")
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self.ensure_connected = cfg.model.ensure_connected
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datadir = cfg.dataset.datadir
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base_path = pathlib.Path(os.path.realpath(__file__)).parents[2]
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meta_filename = os.path.join(base_path, datadir, 'raw', f'{task_name}.meta.json')
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data_root = os.path.join(base_path, datadir, 'raw')
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if os.path.exists(meta_filename):
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with open(meta_filename, 'r') as f:
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meta_dict = json.load(f)
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else:
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meta_dict = compute_meta(data_root, task_name, datamodule.train_index, datamodule.test_index)
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self.base_path = base_path
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self.active_atoms = meta_dict['active_atoms']
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self.max_n_nodes = meta_dict['max_node']
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self.original_max_n_nodes = meta_dict['max_node']
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self.n_nodes = torch.Tensor(meta_dict['n_atoms_per_mol_dist'])
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self.edge_types = torch.Tensor(meta_dict['bond_type_dist'])
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self.transition_E = torch.Tensor(meta_dict['transition_E'])
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self.atom_decoder = meta_dict['active_atoms']
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node_types = torch.Tensor(meta_dict['atom_type_dist'])
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active_index = (node_types > 0).nonzero().squeeze()
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self.node_types = torch.Tensor(meta_dict['atom_type_dist'])[active_index]
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self.nodes_dist = DistributionNodes(self.n_nodes)
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self.active_index = active_index
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val_len = 3 * self.original_max_n_nodes - 2
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meta_val = torch.Tensor(meta_dict['valencies'])
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self.valency_distribution = torch.zeros(val_len)
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val_len = min(val_len, len(meta_val))
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self.valency_distribution[:val_len] = meta_val[:val_len]
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self.y_prior = None
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self.train_ymin = []
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self.train_ymax = []
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def compute_meta(root, source_name, train_index, test_index):
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pt = Chem.GetPeriodicTable()
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atom_name_list = []
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atom_count_list = []
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for i in range(2, 119):
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atom_name_list.append(pt.GetElementSymbol(i))
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atom_count_list.append(0)
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atom_name_list.append('*')
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atom_count_list.append(0)
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n_atoms_per_mol = [0] * 500
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bond_count_list = [0, 0, 0, 0, 0]
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bond_type_to_index = {BT.SINGLE: 1, BT.DOUBLE: 2, BT.TRIPLE: 3, BT.AROMATIC: 4}
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valencies = [0] * 500
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tansition_E = np.zeros((118, 118, 5))
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filename = f'{source_name}.csv.gz'
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df = pd.read_csv(f'{root}/{filename}')
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all_index = list(range(len(df)))
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non_test_index = list(set(all_index) - set(test_index))
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df = df.iloc[non_test_index]
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tot_smiles = df['smiles'].tolist()
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n_atom_list = []
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n_bond_list = []
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for i, sms in enumerate(tot_smiles):
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try:
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mol = Chem.MolFromSmiles(sms)
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except:
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continue
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n_atom = mol.GetNumHeavyAtoms()
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n_bond = mol.GetNumBonds()
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n_atom_list.append(n_atom)
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n_bond_list.append(n_bond)
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n_atoms_per_mol[n_atom] += 1
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cur_atom_count_arr = np.zeros(118)
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for atom in mol.GetAtoms():
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symbol = atom.GetSymbol()
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if symbol == 'H':
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continue
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elif symbol == '*':
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atom_count_list[-1] += 1
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cur_atom_count_arr[-1] += 1
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else:
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atom_count_list[atom.GetAtomicNum()-2] += 1
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cur_atom_count_arr[atom.GetAtomicNum()-2] += 1
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try:
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valencies[int(atom.GetExplicitValence())] += 1
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except:
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print('src', source_name,'int(atom.GetExplicitValence())', int(atom.GetExplicitValence()))
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tansition_E_temp = np.zeros((118, 118, 5))
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for bond in mol.GetBonds():
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start_atom, end_atom = bond.GetBeginAtom(), bond.GetEndAtom()
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if start_atom.GetSymbol() == 'H' or end_atom.GetSymbol() == 'H':
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continue
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|
if start_atom.GetSymbol() == '*':
|
||
|
|
start_index = 117
|
||
|
|
else:
|
||
|
|
start_index = start_atom.GetAtomicNum() - 2
|
||
|
|
if end_atom.GetSymbol() == '*':
|
||
|
|
end_index = 117
|
||
|
|
else:
|
||
|
|
end_index = end_atom.GetAtomicNum() - 2
|
||
|
|
|
||
|
|
bond_type = bond.GetBondType()
|
||
|
|
bond_index = bond_type_to_index[bond_type]
|
||
|
|
bond_count_list[bond_index] += 2
|
||
|
|
|
||
|
|
tansition_E[start_index, end_index, bond_index] += 2
|
||
|
|
tansition_E[end_index, start_index, bond_index] += 2
|
||
|
|
tansition_E_temp[start_index, end_index, bond_index] += 2
|
||
|
|
tansition_E_temp[end_index, start_index, bond_index] += 2
|
||
|
|
|
||
|
|
bond_count_list[0] += n_atom * (n_atom - 1) - n_bond * 2
|
||
|
|
cur_tot_bond = cur_atom_count_arr.reshape(-1,1) * cur_atom_count_arr.reshape(1,-1) * 2 # 118 * 118
|
||
|
|
cur_tot_bond = cur_tot_bond - np.diag(cur_atom_count_arr) * 2 # 118 * 118
|
||
|
|
tansition_E[:, :, 0] += cur_tot_bond - tansition_E_temp.sum(axis=-1)
|
||
|
|
assert (cur_tot_bond > tansition_E_temp.sum(axis=-1)).sum() >= 0, f'i:{i}, sms:{sms}'
|
||
|
|
|
||
|
|
n_atoms_per_mol = np.array(n_atoms_per_mol) / np.sum(n_atoms_per_mol)
|
||
|
|
n_atoms_per_mol = n_atoms_per_mol.tolist()[:51]
|
||
|
|
|
||
|
|
atom_count_list = np.array(atom_count_list) / np.sum(atom_count_list)
|
||
|
|
print('processed meta info: ------', filename, '------')
|
||
|
|
print('len atom_count_list', len(atom_count_list))
|
||
|
|
print('len atom_name_list', len(atom_name_list))
|
||
|
|
active_atoms = np.array(atom_name_list)[atom_count_list > 0]
|
||
|
|
active_atoms = active_atoms.tolist()
|
||
|
|
atom_count_list = atom_count_list.tolist()
|
||
|
|
|
||
|
|
bond_count_list = np.array(bond_count_list) / np.sum(bond_count_list)
|
||
|
|
bond_count_list = bond_count_list.tolist()
|
||
|
|
valencies = np.array(valencies) / np.sum(valencies)
|
||
|
|
valencies = valencies.tolist()
|
||
|
|
|
||
|
|
no_edge = np.sum(tansition_E, axis=-1) == 0
|
||
|
|
first_elt = tansition_E[:, :, 0]
|
||
|
|
first_elt[no_edge] = 1
|
||
|
|
tansition_E[:, :, 0] = first_elt
|
||
|
|
|
||
|
|
tansition_E = tansition_E / np.sum(tansition_E, axis=-1, keepdims=True)
|
||
|
|
|
||
|
|
meta_dict = {
|
||
|
|
'source': source_name,
|
||
|
|
'num_graph': len(n_atom_list),
|
||
|
|
'n_atoms_per_mol_dist': n_atoms_per_mol,
|
||
|
|
'max_node': max(n_atom_list),
|
||
|
|
'max_bond': max(n_bond_list),
|
||
|
|
'atom_type_dist': atom_count_list,
|
||
|
|
'bond_type_dist': bond_count_list,
|
||
|
|
'valencies': valencies,
|
||
|
|
'active_atoms': active_atoms,
|
||
|
|
'num_atom_type': len(active_atoms),
|
||
|
|
'transition_E': tansition_E.tolist(),
|
||
|
|
}
|
||
|
|
|
||
|
|
with open(f'{root}/{source_name}.meta.json', "w") as f:
|
||
|
|
json.dump(meta_dict, f)
|
||
|
|
|
||
|
|
return meta_dict
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
pass
|