wtf
This commit is contained in:
Hanzhang Ma
parent
4f8945ca07
commit
6dc5ef1da8
@ -21,7 +21,7 @@ from sklearn.model_selection import train_test_split
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import utils as utils
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import utils as utils
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from datasets.abstract_dataset import AbstractDatasetInfos, AbstractDataModule
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from datasets.abstract_dataset import AbstractDatasetInfos, AbstractDataModule
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from diffusion.distributions import DistributionNodes
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from diffusion.distributions import DistributionNodes
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from nas_201_api import NASBench201API
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bonds = {BT.SINGLE: 1, BT.DOUBLE: 2, BT.TRIPLE: 3, BT.AROMATIC: 4}
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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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class DataModule(AbstractDataModule):
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@ -48,17 +48,17 @@ class DataModule(AbstractDataModule):
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# Load the dataset to the memory
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# Load the dataset to the memory
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# Dataset has target property, root path, and transform
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# Dataset has target property, root path, and transform
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dataset = Dataset(source=self.task, root=root_path, target_prop=target, transform=None)
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dataset = Dataset(source=self.task, root=root_path, target_prop=target, transform=None)
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print("len dataset", len(dataset))
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# print("len dataset", len(dataset))
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def print_data(dataset):
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# def print_data(dataset):
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print("dataset", dataset)
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# print("dataset", dataset)
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print("dataset keys", dataset.keys)
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# print("dataset keys", dataset.keys)
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print("dataset x", dataset.x)
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# print("dataset x", dataset.x)
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print("dataset edge_index", dataset.edge_index)
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# print("dataset edge_index", dataset.edge_index)
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print("dataset edge_attr", dataset.edge_attr)
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# print("dataset edge_attr", dataset.edge_attr)
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print("dataset y", dataset.y)
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# print("dataset y", dataset.y)
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print("")
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# print("")
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print_data(dataset=dataset[0])
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# print_data(dataset=dataset[0])
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print_data(dataset=dataset[1])
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# print_data(dataset=dataset[1])
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if len(self.task.split('-')) == 2:
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if len(self.task.split('-')) == 2:
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@ -155,7 +155,30 @@ class Dataset(InMemoryDataset):
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def processed_file_names(self):
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def processed_file_names(self):
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return [f'{self.source}.pt']
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return [f'{self.source}.pt']
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def create_adj_matrix_and_ops(nodes, edges):
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num_nodes = len(nodes)
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adj_matrix = np.zeros((num_nodes, num_nodes), dtype=int)
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for (src, dst) in edges:
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adj_matrix[src][dst] = 1
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return adj_matrix, nodes
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def parse_architecture_string(arch_str):
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print(arch_str)
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steps = arch_str.split('+')
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nodes = ['input'] # Start with input node
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edges = []
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for i, step in enumerate(steps):
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step = step.strip('|').split('|')
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for node in step:
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op, idx = node.split('~')
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edges.append((int(idx), i+1)) # i+1 because 0 is input node
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nodes.append(op)
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nodes.append('output') # Add output node
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return nodes, edges
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def process(self):
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def process(self):
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# return
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api = NASBench201API('./NAS-Bench-201-v1_1-096897.pth')
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RDLogger.DisableLog('rdApp.*')
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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_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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data_df = pd.read_csv(data_path)
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@ -200,26 +223,65 @@ class Dataset(InMemoryDataset):
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return data, active_atoms
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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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# 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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len_data = 15625
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data_list = []
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data_list = []
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len_data = len(data_df)
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bonds = {
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with tqdm(total=len_data) as pbar:
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'nor_conv_1x1': 1,
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# --- data processing start ---
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'nor_conv_3x3': 2,
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active_atoms = set()
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'avg_pool_3x3': 3,
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for i, (sms, df_row) in enumerate(data_df.iterrows()):
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'skip_connect': 4,
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if i == sms:
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'input': 0,
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sms = df_row['smiles']
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'output': 5
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mol = Chem.MolFromSmiles(sms, sanitize=False)
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}
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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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def arch_to_graph(arch_str, sa, sc, target, target2=None, target3=None):
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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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nodes, edges = Dataset.parse_architecture_string(arch_str)
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elif len(self.target_prop.split('-')) == 3:
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node_labels = [bonds[node] for node in nodes] # Replace with appropriate encoding if necessary
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target1, target2, target3 = self.target_prop.split('-')
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x = torch.LongTensor(node_labels)
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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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edges_list = [(start, end) for start, end in edges]
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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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edge_type = [bonds[nodes[end]] for start, end in edges] # Example: using end node type as edge type
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active_atoms.update(cur_active_atoms)
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edge_index = torch.tensor(edges_list, dtype=torch.long).t().contiguous()
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data_list.append(data)
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edge_type = torch.tensor(edge_type, dtype=torch.long)
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pbar.update(1)
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edge_attr = edge_type.view(-1, 1)
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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)
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return data, nodes
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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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for arch_index in range(len_data):
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arch_info = api.get_arch(arch_index)
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arch_str = arch_info['arch_str']
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nodes, edges = Dataset.parse_architecture_string(arch_str)
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adj_matrix, nodes = Dataset.create_adj_matrix_and_ops(nodes, edges)
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data, cur_active_atoms = graph
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torch.save(self.collate(data_list), self.processed_paths[0])
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torch.save(self.collate(data_list), self.processed_paths[0])
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@ -234,8 +296,10 @@ class DataInfos(AbstractDatasetInfos):
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'N2': 'regression',
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'N2': 'regression',
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'CO2': 'regression',
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'CO2': 'regression',
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}
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}
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task_name = cfg.dataset.task_name
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task_name = cfg.dataset.task_name
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self.task = task_name
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self.task = task_name
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print(self.task)
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self.task_type = tasktype_dict.get(task_name, "regression")
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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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self.ensure_connected = cfg.model.ensure_connected
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@ -409,6 +473,181 @@ def compute_meta(root, source_name, train_index, test_index):
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return meta_dict
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return meta_dict
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op_to_atom = {
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'input': 'Si', # Hydrogen for input
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'nor_conv_1x1': 'C', # Carbon for 1x1 convolution
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'nor_conv_3x3': 'N', # Nitrogen for 3x3 convolution
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'avg_pool_3x3': 'O', # Oxygen for 3x3 average pooling
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'skip_connect': 'P', # Phosphorus for skip connection
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'none': 'S', # Sulfur for no operation
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'output': 'He' # Helium for output
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}
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def get_sample_nasbench_graph():
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adj_mat = np.array([[0, 1, 1, 0, 1, 0, 0, 0],
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[0, 0, 0, 1, 0, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 0]])
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ops = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
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return adj_mat, ops
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def nasbench_to_molecule(adj_mat, ops):
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mol = Chem.RWMol() # Create a new editable molecule
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atom_map = {} # Map to keep track of node to atom mapping
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# Add atoms to the molecule
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for i, op in enumerate(ops):
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atom_type = op_to_atom.get(op, 'C') # Default to Carbon if operation not found
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atom = Chem.Atom(atom_type) # Create an atom of the specified type
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idx = mol.AddAtom(atom)
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atom_map[i] = idx
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# Add bonds to the molecule
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for i in range(adj_mat.shape[0]):
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for j in range(adj_mat.shape[1]):
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if adj_mat[i, j] == 1:
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mol.AddBond(atom_map[i], atom_map[j], Chem.rdchem.BondType.SINGLE)
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return mol
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def compute_meta_graph(root, source_name, train_index, test_index):
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# initialize the periodic table
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# 118 elements + 1 for *
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# Initializes arrays to count the number of atoms per molecule, bond types, valencies, and transition probabilities between atom types.
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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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# Load the data from the source file
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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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# extract the smiles from the dataframe
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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() == '*':
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start_index = 117
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else:
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start_index = start_atom.GetAtomicNum() - 2
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if end_atom.GetSymbol() == '*':
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end_index = 117
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else:
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end_index = end_atom.GetAtomicNum() - 2
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bond_type = bond.GetBondType()
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bond_index = bond_type_to_index[bond_type]
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bond_count_list[bond_index] += 2
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# Update the transition matrix
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# The transition matrix is symmetric, so we update both directions
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# We also update the temporary transition matrix to check for errors
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# in the atom count
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tansition_E[start_index, end_index, bond_index] += 2
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tansition_E[end_index, start_index, bond_index] += 2
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tansition_E_temp[start_index, end_index, bond_index] += 2
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tansition_E_temp[end_index, start_index, bond_index] += 2
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bond_count_list[0] += n_atom * (n_atom - 1) - n_bond * 2
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cur_tot_bond = cur_atom_count_arr.reshape(-1,1) * cur_atom_count_arr.reshape(1,-1) * 2 # 118 * 118
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cur_tot_bond = cur_tot_bond - np.diag(cur_atom_count_arr) * 2 # 118 * 118
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tansition_E[:, :, 0] += cur_tot_bond - tansition_E_temp.sum(axis=-1)
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assert (cur_tot_bond > tansition_E_temp.sum(axis=-1)).sum() >= 0, f'i:{i}, sms:{sms}'
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n_atoms_per_mol = np.array(n_atoms_per_mol) / np.sum(n_atoms_per_mol)
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n_atoms_per_mol = n_atoms_per_mol.tolist()[:51]
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atom_count_list = np.array(atom_count_list) / np.sum(atom_count_list)
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print('processed meta info: ------', filename, '------')
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print('len atom_count_list', len(atom_count_list))
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print('len atom_name_list', len(atom_name_list))
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active_atoms = np.array(atom_name_list)[atom_count_list > 0]
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active_atoms = active_atoms.tolist()
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atom_count_list = atom_count_list.tolist()
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bond_count_list = np.array(bond_count_list) / np.sum(bond_count_list)
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bond_count_list = bond_count_list.tolist()
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valencies = np.array(valencies) / np.sum(valencies)
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valencies = valencies.tolist()
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no_edge = np.sum(tansition_E, axis=-1) == 0
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first_elt = tansition_E[:, :, 0]
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first_elt[no_edge] = 1
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tansition_E[:, :, 0] = first_elt
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tansition_E = tansition_E / np.sum(tansition_E, axis=-1, keepdims=True)
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|
||||||
|
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__":
|
if __name__ == "__main__":
|
||||||
pass
|
pass
|
||||||
Loading…
Reference in New Issue
Block a user