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Hanzhang Ma
2024-06-09 22:44:16 +02:00
parent 4f8945ca07
commit 6dc5ef1da8
1 changed files with 270 additions and 31 deletions
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301
graph_dit/datasets/dataset.py
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@@ -21,7 +21,7 @@ from sklearn.model_selection import train_test_split
import utils as utils
from datasets.abstract_dataset import AbstractDatasetInfos, AbstractDataModule
from diffusion.distributions import DistributionNodes
from nas_201_api import NASBench201API
bonds = {BT.SINGLE: 1, BT.DOUBLE: 2, BT.TRIPLE: 3, BT.AROMATIC: 4}
class DataModule(AbstractDataModule):
@@ -48,17 +48,17 @@ class DataModule(AbstractDataModule):
# Load the dataset to the memory
# Dataset has target property, root path, and transform
dataset = Dataset(source=self.task, root=root_path, target_prop=target, transform=None)
print("len dataset", len(dataset))
def print_data(dataset):
print("dataset", dataset)
print("dataset keys", dataset.keys)
print("dataset x", dataset.x)
print("dataset edge_index", dataset.edge_index)
print("dataset edge_attr", dataset.edge_attr)
print("dataset y", dataset.y)
print("")
print_data(dataset=dataset[0])
print_data(dataset=dataset[1])
# print("len dataset", len(dataset))
# def print_data(dataset):
# print("dataset", dataset)
# print("dataset keys", dataset.keys)
# print("dataset x", dataset.x)
# print("dataset edge_index", dataset.edge_index)
# print("dataset edge_attr", dataset.edge_attr)
# print("dataset y", dataset.y)
# print("")
# print_data(dataset=dataset[0])
# print_data(dataset=dataset[1])
if len(self.task.split('-')) == 2:
@@ -155,7 +155,30 @@ class Dataset(InMemoryDataset):
def processed_file_names(self):
return [f'{self.source}.pt']
def create_adj_matrix_and_ops(nodes, edges):
num_nodes = len(nodes)
adj_matrix = np.zeros((num_nodes, num_nodes), dtype=int)
for (src, dst) in edges:
adj_matrix[src][dst] = 1
return adj_matrix, nodes
def parse_architecture_string(arch_str):
print(arch_str)
steps = arch_str.split('+')
nodes = ['input'] # Start with input node
edges = []
for i, step in enumerate(steps):
step = step.strip('|').split('|')
for node in step:
op, idx = node.split('~')
edges.append((int(idx), i+1)) # i+1 because 0 is input node
nodes.append(op)
nodes.append('output') # Add output node
return nodes, edges
def process(self):
# return
api = NASBench201API('./NAS-Bench-201-v1_1-096897.pth')
RDLogger.DisableLog('rdApp.*')
data_path = osp.join(self.raw_dir, self.raw_file_names[0])
data_df = pd.read_csv(data_path)
@@ -200,26 +223,65 @@ class Dataset(InMemoryDataset):
return data, active_atoms
# Loop through every row in the DataFrame and apply the function
# data_list = []
# len_data = len(data_df)
len_data = 15625
data_list = []
len_data = len(data_df)
with tqdm(total=len_data) as pbar:
# --- data processing start ---
active_atoms = set()
for i, (sms, df_row) in enumerate(data_df.iterrows()):
if i == sms:
sms = df_row['smiles']
mol = Chem.MolFromSmiles(sms, sanitize=False)
if len(self.target_prop.split('-')) == 2:
target1, target2 = self.target_prop.split('-')
data, cur_active_atoms = mol_to_graph(mol, df_row['SA'], df_row['SC'], df_row[target1], target2=df_row[target2])
elif len(self.target_prop.split('-')) == 3:
target1, target2, target3 = self.target_prop.split('-')
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])
else:
data, cur_active_atoms = mol_to_graph(mol, df_row['SA'], df_row['SC'], df_row[self.target_prop])
active_atoms.update(cur_active_atoms)
data_list.append(data)
pbar.update(1)
bonds = {
'nor_conv_1x1': 1,
'nor_conv_3x3': 2,
'avg_pool_3x3': 3,
'skip_connect': 4,
'input': 0,
'output': 5
}
def arch_to_graph(arch_str, sa, sc, target, target2=None, target3=None):
nodes, edges = Dataset.parse_architecture_string(arch_str)
node_labels = [bonds[node] for node in nodes] # Replace with appropriate encoding if necessary
x = torch.LongTensor(node_labels)
edges_list = [(start, end) for start, end in edges]
edge_type = [bonds[nodes[end]] for start, end in edges] # Example: using end node type as edge type
edge_index = torch.tensor(edges_list, dtype=torch.long).t().contiguous()
edge_type = torch.tensor(edge_type, dtype=torch.long)
edge_attr = edge_type.view(-1, 1)
if target3 is not None:
y = torch.tensor([sa, sc, target, target2, target3], dtype=torch.float).view(1, -1)
elif target2 is not None:
y = torch.tensor([sa, sc, target, target2], dtype=torch.float).view(1, -1)
else:
y = torch.tensor([sa, sc, target], dtype=torch.float).view(1, -1)
data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y)
return data, nodes
# --- data processing start ---
# active_atoms = set()
# for i, (sms, df_row) in enumerate(data_df.iterrows()):
# if i == sms:
# sms = df_row['smiles']
# mol = Chem.MolFromSmiles(sms, sanitize=False)
# if len(self.target_prop.split('-')) == 2:
# target1, target2 = self.target_prop.split('-')
# data, cur_active_atoms = mol_to_graph(mol, df_row['SA'], df_row['SC'], df_row[target1], target2=df_row[target2])
# elif len(self.target_prop.split('-')) == 3:
# target1, target2, target3 = self.target_prop.split('-')
# 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])
# else:
# data, cur_active_atoms = mol_to_graph(mol, df_row['SA'], df_row['SC'], df_row[self.target_prop])
# active_atoms.update(cur_active_atoms)
# data_list.append(data)
# pbar.update(1)
for arch_index in range(len_data):
arch_info = api.get_arch(arch_index)
arch_str = arch_info['arch_str']
nodes, edges = Dataset.parse_architecture_string(arch_str)
adj_matrix, nodes = Dataset.create_adj_matrix_and_ops(nodes, edges)
data, cur_active_atoms = graph
torch.save(self.collate(data_list), self.processed_paths[0])
@@ -234,8 +296,10 @@ class DataInfos(AbstractDatasetInfos):
'N2': 'regression',
'CO2': 'regression',
}
task_name = cfg.dataset.task_name
self.task = task_name
print(self.task)
self.task_type = tasktype_dict.get(task_name, "regression")
self.ensure_connected = cfg.model.ensure_connected
@@ -409,6 +473,181 @@ def compute_meta(root, source_name, train_index, test_index):
return meta_dict
op_to_atom = {
'input': 'Si', # Hydrogen for input
'nor_conv_1x1': 'C', # Carbon for 1x1 convolution
'nor_conv_3x3': 'N', # Nitrogen for 3x3 convolution
'avg_pool_3x3': 'O', # Oxygen for 3x3 average pooling
'skip_connect': 'P', # Phosphorus for skip connection
'none': 'S', # Sulfur for no operation
'output': 'He' # Helium for output
}
def get_sample_nasbench_graph():
adj_mat = np.array([[0, 1, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0]])
ops = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
return adj_mat, ops
def nasbench_to_molecule(adj_mat, ops):
mol = Chem.RWMol() # Create a new editable molecule
atom_map = {} # Map to keep track of node to atom mapping
# Add atoms to the molecule
for i, op in enumerate(ops):
atom_type = op_to_atom.get(op, 'C') # Default to Carbon if operation not found
atom = Chem.Atom(atom_type) # Create an atom of the specified type
idx = mol.AddAtom(atom)
atom_map[i] = idx
# Add bonds to the molecule
for i in range(adj_mat.shape[0]):
for j in range(adj_mat.shape[1]):
if adj_mat[i, j] == 1:
mol.AddBond(atom_map[i], atom_map[j], Chem.rdchem.BondType.SINGLE)
return mol
def compute_meta_graph(root, source_name, train_index, test_index):
# initialize the periodic table
# 118 elements + 1 for *
# Initializes arrays to count the number of atoms per molecule, bond types, valencies, and transition probabilities between atom types.
pt = Chem.GetPeriodicTable()
atom_name_list = []
atom_count_list = []
for i in range(2, 119):
atom_name_list.append(pt.GetElementSymbol(i))
atom_count_list.append(0)
atom_name_list.append('*')
atom_count_list.append(0)
n_atoms_per_mol = [0] * 500
bond_count_list = [0, 0, 0, 0, 0]
bond_type_to_index = {BT.SINGLE: 1, BT.DOUBLE: 2, BT.TRIPLE: 3, BT.AROMATIC: 4}
valencies = [0] * 500
tansition_E = np.zeros((118, 118, 5))
# Load the data from the source file
filename = f'{source_name}.csv.gz'
df = pd.read_csv(f'{root}/{filename}')
all_index = list(range(len(df)))
non_test_index = list(set(all_index) - set(test_index))
df = df.iloc[non_test_index]
# extract the smiles from the dataframe
tot_smiles = df['smiles'].tolist()
n_atom_list = []
n_bond_list = []
for i, sms in enumerate(tot_smiles):
try:
mol = Chem.MolFromSmiles(sms)
except:
continue
n_atom = mol.GetNumHeavyAtoms()
n_bond = mol.GetNumBonds()
n_atom_list.append(n_atom)
n_bond_list.append(n_bond)
n_atoms_per_mol[n_atom] += 1
cur_atom_count_arr = np.zeros(118)
for atom in mol.GetAtoms():
symbol = atom.GetSymbol()
if symbol == 'H':
continue
elif symbol == '*':
atom_count_list[-1] += 1
cur_atom_count_arr[-1] += 1
else:
atom_count_list[atom.GetAtomicNum()-2] += 1
cur_atom_count_arr[atom.GetAtomicNum()-2] += 1
try:
valencies[int(atom.GetExplicitValence())] += 1
except:
print('src', source_name,'int(atom.GetExplicitValence())', int(atom.GetExplicitValence()))
tansition_E_temp = np.zeros((118, 118, 5))
for bond in mol.GetBonds():
start_atom, end_atom = bond.GetBeginAtom(), bond.GetEndAtom()
if start_atom.GetSymbol() == 'H' or end_atom.GetSymbol() == 'H':
continue
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
# Update the transition matrix
# The transition matrix is symmetric, so we update both directions
# We also update the temporary transition matrix to check for errors
# in the atom count
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