AutoGL Model

AutoGL project uses model to define the common graph nerual networks and automodel to denote the relative class that includes some auto functions. Currently, we support the following models and automodels:

And we also support the following models and automodels for graph classification tasks: * GIN and AutoGIN : graph isomorphism network from * Topkpool and AutoTopkpool : graph U-Net from,

Define your own model and automodel

If you want to add your own model and automodel for some task, the only thing you should do is add a new model where the forward function should be fulfilled and a new automodel inherited from the basemodel.

Firstly, you should define your model if it does not belong to the models above.

Secondly, you should define your corresponding automodel.

# 1. define your search space to of your automodel instance
    {'parameterName': 'num_layers', 'type': 'DISCRETE', 'feasiblePoints': '2,3,4'},
    {"parameterName": 'hidden', "type": "NUMERICAL_LIST", "numericalType": "INTEGER", "length": 3, "minValue": [8, 8, 8], "maxValue": [64, 64, 64], "scalingType": "LOG"},
    {'parameterName': 'dropout', 'type': 'DOUBLE', 'maxValue': 0.9, 'minValue': 0.1, 'scalingType': 'LINEAR'},
    {'parameterName': 'act', 'type': 'CATEGORICAL_LIST', "feasiblePoints": ['leaky_relu', 'relu', 'elu', 'tanh']},
# 2. define the default point to self.hyperparams of your automodel instance
    'num_layers': 2,
    'hidden': [16],
    'dropout': 0.2,
    'act': 'leaky_relu'

Where is a list of dictionary indicating the name, type, feasible point, min/max value and some properties of the parameter. self.hyperparams is a dictionary indicating the hyper-parameters used in this model.

Finally, you can use the defined model and automodel for the specific need.

# for example
import torch
from .base import BaseModel
class YourGNN(torch.nn.Module):
    def forward(self, data):
        pass  # Your forward function

class YourAutoGNN(BaseModel):
    def __init__(self, num_features=None, num_classes=None, device=None, init=True, **args):
        num_features: the number of features
        num_classes: the number of classes
        device: your device to run code
        init: if True, the model will be initialize
        """ = XXX  # Define your search space
        self.hyperparams = XXX  # Define your hyper-parameters
        self.initialized = False
        if init is True: