Requirements

python == 3.7.4
torch == 1.6.0
torch-cluster == 1.5.7  
torch_scatter == 2.0.6  
torch-sparse == 0.6.9
torch-geometric == 2.0.0
pandas == 1.2.5
numpy == 1.17.2  

Installation

conda create -n DAGC python==3.7.4
conda activate DAGC
pip install torch==1.6.0 -f https://download.pytorch.org/whl/cu101/torch-1.6.0%2Bcu101-cp37-cp37m-linux_x86_64.whl
pip install torch-cluster==1.5.7 -f https://data.pyg.org/whl/torch-1.6.0%2Bcu101.html
pip install torch_scatter==2.0.6 -f https://data.pyg.org/whl/torch-1.6.0%2Bcu101.html
pip install torch-sparse==0.6.9 -f https://data.pyg.org/whl/torch-1.6.0%2Bcu101.html
pip install torch-geometric==2.0.0
pip install pandas==1.2.5
pip install numpy==1.17.2

Quick Start

A quick start example is given by:

$ python auto_test.py --data_name MUTAG --gpu 0

An example of auto search is as follows:

$ python auto_main.py --data_name MUTAG --gpu 0
or
$ python auto_main.py --data_name COX2 --gpu 0

Arguments Description

auto_main.py/auto_test.py

Name	Default value	Description
data_name	MUTAG	the name of dataset for graph classification
gpu	0	gpu device id
epochs	100	the num of training epochs of each GNN architecture to obtain the reward value of each GNN architecture during auto-search
epochs_test	100	the num of training epochs during testing the optimal GNN architecture, which will be trained from scratch with 20-fold cross-validation
controller_train_epoch	200	the train epoch of learnable agent based on reinforcement learning
search_scale	100	the number of good GNN architecture predicted by the trained learnable agent

User-defined

DAGC is very friendly for users to implement customization, users can freely define their own functional components as long as they follow the custom specification. Users can know the custom specification of each functional component in the following list, which is very simple. The list of definable components is as follows:

Graph Data

First, user can define the input graph data if user use non benchmark datasets. The data format is like torch-geometric:

from torch_geometric.data import InMemoryDataset
from torch_geometric.data import Data
import torch

class PyGDataset(InMemoryDataset):
    """
    data: list
    every element is a sample, in which contain feature, torch_edge_index, label
    
    dataset_save_path: str
    the saving path for dataset
    """
    def __init__(self, data, dataset_save_path):
        self.dataset = self.convert_pyg_data(data)
        self.dataset_save_path = dataset_save_path
        if not os.path.exists(self.dataset_save_path):
            os.mkdir(self.dataset_save_path)
        if os.path.exists(self.dataset_save_path + 'processed/' + self.processed_file_names[0]):
            os.remove(self.dataset_save_path + 'processed/' + self.processed_file_names[0])
        
        super(PyGDataset, self).__init__(root=self.dataset_save_path)
        self.data, self.slices = torch.load(self.processed_paths[0])
    
    def convert_pyg_data(self, data):
        converted_data = []
        for d in data:
            feature = d[0]
            torch_edge_index = d[1]
            label = d[2]
            c_d = Data(x=feature, edge_index=torch_edge_index, y=label)
            converted_data.append(c_d)
        return converted_data
  
    @property
    def processed_file_names(self):
        return ['data.pt']
        
    def process(self):
        data_list = self.dataset
        data, slices = self.collate(data_list)
        print("Saving processed files...")
        torch.save((data, slices), self.processed_paths[0])
        print('Saving complete!')

Search Space

Then, user can modify the search space according to the need. The default setting of search space is as follows:

Default Configuration

Search Component	Candidate Operations
GNN Depth	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Aggregation Function	GCNConv, GATConv, SAGEConv, GINConv, GraphConv, GeneralConv
Local Pooling	TopKPool, SAGPool, ASAPool, PANPool, HopPool, GCPool, GAPPool, None
Global Pooling	GlobalMaxPool, GlobalMeanPool, GlobalSumPool

Modify GNN Depth Component

modify the 'gnn_layers' key in the search_space_config.py

Modify Aggregation Function Component

modify the 'conv' key in the search_space_config.py

and, define the added aggregation function like convolution.py

last, register to the 'conv_map' function in utils.py

Modify Local Pooling Component

modify the 'local_pooling' key in the search_space_config.py

and, define the added local pooling function like local_pooling.py

last, register to the 'local_pooling_map' function in utils.py

Modify Global Pooling Component

modify the 'global_pooling' key in the search_space_config.py

and, define the added global pooling function like torch_geometric.nn.pool.glob

last, register to the 'global_pooling_map' function in utils.py