Loading deepchem/models/torch_models/gcn.py +58 −29 Original line number Diff line number Diff line Loading @@ -46,30 +46,43 @@ class GCN(nn.Module): ----- This class requires DGL (https://github.com/dmlc/dgl) and DGL-LifeSci (https://github.com/awslabs/dgl-lifesci) to be installed. This model is different from deepchem.models.GraphConvModel as follows: * For each graph convolution, the learnable weight in this model is shared across all nodes. ``GraphConvModel`` employs separate learnable weights for nodes of different degrees. A learnable weight is shared across all nodes of a particular degree. * For ``GraphConvModel``, there is an additional GraphPool operation after each graph convolution. The operation updates the representation of a node by applying an element-wise maximum over the representations of its neighbors and itself. * For computing graph-level representations, this model computes a weighted sum and an element-wise maximum of the representations of all nodes in a graph and concatenates them. The node weights are obtained by using a linear/dense layer followd by a sigmoid function. For ``GraphConvModel``, the sum over node representations is unweighted. * There are various minor differences in using dropout, skip connection and batch normalization. """ def __init__(self, in_node_dim: int, hidden_node_dim: int, num_gnn_layers: int, n_tasks: int, graph_conv_layers: list = None, activation = None, residual: bool = True, batchnorm: bool = False, dropout: float = 0., predictor_hidden_feats: int = 128, predictor_dropout: float = 0., n_tasks: int = 1, mode: str = 'regression', number_atom_features: int = 75, n_classes: int = 2, nfeat_name: str = 'x'): """ Parameters ---------- in_node_dim: int The length of the initial node feature vectors. hidden_node_dim: int The length of the hidden node feature vectors. num_gnn_layers: int The number of GCN layers. n_tasks: int Number of tasks. graph_conv_layers: list of int Width of channels for GCN layers. graph_conv_layers[i] gives the width of channel for the i-th GCN layer. If not specified, the default value will be [64, 64]. activation: callable The activation function to apply to the output of each GCN layer. By default, no activation function will be applied. Loading @@ -84,10 +97,10 @@ class GCN(nn.Module): The size for hidden representations in the output MLP predictor. Default to 128. predictor_dropout: float The dropout probability in the output MLP predictor. Default to 0. n_tasks: int The output size. mode: str The model type, 'classification' or 'regression'. number_atom_features: int The length of the initial atom feature vectors. Default to 75. n_classes: int The number of classes to predict per task (only used when ``mode`` is 'classification'). Loading Loading @@ -120,11 +133,15 @@ class GCN(nn.Module): from dgllife.model import GCNPredictor as DGLGCNPredictor if graph_conv_layers is None: graph_conv_layers = [64, 64] num_gnn_layers = len(graph_conv_layers) if activation is not None: activation = [activation] * num_gnn_layers self.model = DGLGCNPredictor(in_feats=in_node_dim, hidden_feats=[hidden_node_dim] * num_gnn_layers, self.model = DGLGCNPredictor(in_feats=number_atom_features, hidden_feats=graph_conv_layers, activation=activation, residual=[residual] * num_gnn_layers, batchnorm=[batchnorm] * num_gnn_layers, Loading Loading @@ -204,31 +221,44 @@ class GCNModel(TorchModel): ----- This class requires DGL (https://github.com/dmlc/dgl) and DGL-LifeSci (https://github.com/awslabs/dgl-lifesci) to be installed. This model is different from deepchem.models.GraphConvModel as follows: * For each graph convolution, the learnable weight in this model is shared across all nodes. ``GraphConvModel`` employs separate learnable weights for nodes of different degrees. A learnable weight is shared across all nodes of a particular degree. * For ``GraphConvModel``, there is an additional GraphPool operation after each graph convolution. The operation updates the representation of a node by applying an element-wise maximum over the representations of its neighbors and itself. * For computing graph-level representations, this model computes a weighted sum and an element-wise maximum of the representations of all nodes in a graph and concatenates them. The node weights are obtained by using a linear/dense layer followd by a sigmoid function. For ``GraphConvModel``, the sum over node representations is unweighted. * There are various minor differences in using dropout, skip connection and batch normalization. """ def __init__(self, in_node_dim: int, hidden_node_dim: int, num_gnn_layers: int, n_tasks: int, graph_conv_layers: list = None, activation = None, residual: bool = True, batchnorm: bool = False, dropout: float = 0., predictor_hidden_feats: int = 128, predictor_dropout: float = 0., n_tasks: int = 1, mode: str = 'regression', number_atom_features=75, n_classes: int = 2, nfeat_name: str = 'x', **kwargs): """ Parameters ---------- in_node_dim: int The length of the initial node feature vectors. hidden_node_dim: int The length of the hidden node feature vectors. num_gnn_layers: int The number of GCN layers. n_tasks: int Number of tasks. graph_conv_layers: list of int Width of channels for GCN layers. graph_conv_layers[i] gives the width of channel for the i-th GCN layer. If not specified, the default value will be [64, 64]. activation: callable The activation function to apply to the output of each GCN layer. By default, no activation function will be applied. Loading @@ -243,10 +273,10 @@ class GCNModel(TorchModel): The size for hidden representations in the output MLP predictor. Default to 128. predictor_dropout: float The dropout probability in the output MLP predictor. Default to 0. n_tasks: int The output size. mode: str The model type, 'classification' or 'regression'. number_atom_features: int The length of the initial atom feature vectors. Default to 75. n_classes: int The number of classes to predict per task (only used when ``mode`` is 'classification'). Loading @@ -256,9 +286,7 @@ class GCNModel(TorchModel): kwargs This can include any keyword argument of TorchModel. """ model = GCN(in_node_dim=in_node_dim, hidden_node_dim=hidden_node_dim, num_gnn_layers=num_gnn_layers, model = GCN(graph_conv_layers=graph_conv_layers, activation=activation, residual=residual, batchnorm=batchnorm, Loading @@ -267,6 +295,7 @@ class GCNModel(TorchModel): predictor_dropout=predictor_dropout, n_tasks=n_tasks, mode=mode, number_atom_features=number_atom_features, n_classes=n_classes, nfeat_name=nfeat_name) if mode == 'regression': Loading docs/models.rst +9 −0 Original line number Diff line number Diff line Loading @@ -126,6 +126,9 @@ read off what's needed to train the model from the table below. | :code:`GATModel` | Classifier/| :code:`GraphData` | | :code:`MolGraphConvFeaturizer` | :code:`fit` | | | Regressor | | | | | +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ | :code:`GCNModel` | Classifier/| :code:`GraphData` | | :code:`CGCNNFeaturizer` | :code:`fit` | | | Regressor | | | | | +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ Model ----- Loading Loading @@ -447,3 +450,9 @@ GATModel .. autoclass:: deepchem.models.GATModel :members: GCNModel -------- .. autoclass:: deepchem.models.GCNModel :members: Loading
deepchem/models/torch_models/gcn.py +58 −29 Original line number Diff line number Diff line Loading @@ -46,30 +46,43 @@ class GCN(nn.Module): ----- This class requires DGL (https://github.com/dmlc/dgl) and DGL-LifeSci (https://github.com/awslabs/dgl-lifesci) to be installed. This model is different from deepchem.models.GraphConvModel as follows: * For each graph convolution, the learnable weight in this model is shared across all nodes. ``GraphConvModel`` employs separate learnable weights for nodes of different degrees. A learnable weight is shared across all nodes of a particular degree. * For ``GraphConvModel``, there is an additional GraphPool operation after each graph convolution. The operation updates the representation of a node by applying an element-wise maximum over the representations of its neighbors and itself. * For computing graph-level representations, this model computes a weighted sum and an element-wise maximum of the representations of all nodes in a graph and concatenates them. The node weights are obtained by using a linear/dense layer followd by a sigmoid function. For ``GraphConvModel``, the sum over node representations is unweighted. * There are various minor differences in using dropout, skip connection and batch normalization. """ def __init__(self, in_node_dim: int, hidden_node_dim: int, num_gnn_layers: int, n_tasks: int, graph_conv_layers: list = None, activation = None, residual: bool = True, batchnorm: bool = False, dropout: float = 0., predictor_hidden_feats: int = 128, predictor_dropout: float = 0., n_tasks: int = 1, mode: str = 'regression', number_atom_features: int = 75, n_classes: int = 2, nfeat_name: str = 'x'): """ Parameters ---------- in_node_dim: int The length of the initial node feature vectors. hidden_node_dim: int The length of the hidden node feature vectors. num_gnn_layers: int The number of GCN layers. n_tasks: int Number of tasks. graph_conv_layers: list of int Width of channels for GCN layers. graph_conv_layers[i] gives the width of channel for the i-th GCN layer. If not specified, the default value will be [64, 64]. activation: callable The activation function to apply to the output of each GCN layer. By default, no activation function will be applied. Loading @@ -84,10 +97,10 @@ class GCN(nn.Module): The size for hidden representations in the output MLP predictor. Default to 128. predictor_dropout: float The dropout probability in the output MLP predictor. Default to 0. n_tasks: int The output size. mode: str The model type, 'classification' or 'regression'. number_atom_features: int The length of the initial atom feature vectors. Default to 75. n_classes: int The number of classes to predict per task (only used when ``mode`` is 'classification'). Loading Loading @@ -120,11 +133,15 @@ class GCN(nn.Module): from dgllife.model import GCNPredictor as DGLGCNPredictor if graph_conv_layers is None: graph_conv_layers = [64, 64] num_gnn_layers = len(graph_conv_layers) if activation is not None: activation = [activation] * num_gnn_layers self.model = DGLGCNPredictor(in_feats=in_node_dim, hidden_feats=[hidden_node_dim] * num_gnn_layers, self.model = DGLGCNPredictor(in_feats=number_atom_features, hidden_feats=graph_conv_layers, activation=activation, residual=[residual] * num_gnn_layers, batchnorm=[batchnorm] * num_gnn_layers, Loading Loading @@ -204,31 +221,44 @@ class GCNModel(TorchModel): ----- This class requires DGL (https://github.com/dmlc/dgl) and DGL-LifeSci (https://github.com/awslabs/dgl-lifesci) to be installed. This model is different from deepchem.models.GraphConvModel as follows: * For each graph convolution, the learnable weight in this model is shared across all nodes. ``GraphConvModel`` employs separate learnable weights for nodes of different degrees. A learnable weight is shared across all nodes of a particular degree. * For ``GraphConvModel``, there is an additional GraphPool operation after each graph convolution. The operation updates the representation of a node by applying an element-wise maximum over the representations of its neighbors and itself. * For computing graph-level representations, this model computes a weighted sum and an element-wise maximum of the representations of all nodes in a graph and concatenates them. The node weights are obtained by using a linear/dense layer followd by a sigmoid function. For ``GraphConvModel``, the sum over node representations is unweighted. * There are various minor differences in using dropout, skip connection and batch normalization. """ def __init__(self, in_node_dim: int, hidden_node_dim: int, num_gnn_layers: int, n_tasks: int, graph_conv_layers: list = None, activation = None, residual: bool = True, batchnorm: bool = False, dropout: float = 0., predictor_hidden_feats: int = 128, predictor_dropout: float = 0., n_tasks: int = 1, mode: str = 'regression', number_atom_features=75, n_classes: int = 2, nfeat_name: str = 'x', **kwargs): """ Parameters ---------- in_node_dim: int The length of the initial node feature vectors. hidden_node_dim: int The length of the hidden node feature vectors. num_gnn_layers: int The number of GCN layers. n_tasks: int Number of tasks. graph_conv_layers: list of int Width of channels for GCN layers. graph_conv_layers[i] gives the width of channel for the i-th GCN layer. If not specified, the default value will be [64, 64]. activation: callable The activation function to apply to the output of each GCN layer. By default, no activation function will be applied. Loading @@ -243,10 +273,10 @@ class GCNModel(TorchModel): The size for hidden representations in the output MLP predictor. Default to 128. predictor_dropout: float The dropout probability in the output MLP predictor. Default to 0. n_tasks: int The output size. mode: str The model type, 'classification' or 'regression'. number_atom_features: int The length of the initial atom feature vectors. Default to 75. n_classes: int The number of classes to predict per task (only used when ``mode`` is 'classification'). Loading @@ -256,9 +286,7 @@ class GCNModel(TorchModel): kwargs This can include any keyword argument of TorchModel. """ model = GCN(in_node_dim=in_node_dim, hidden_node_dim=hidden_node_dim, num_gnn_layers=num_gnn_layers, model = GCN(graph_conv_layers=graph_conv_layers, activation=activation, residual=residual, batchnorm=batchnorm, Loading @@ -267,6 +295,7 @@ class GCNModel(TorchModel): predictor_dropout=predictor_dropout, n_tasks=n_tasks, mode=mode, number_atom_features=number_atom_features, n_classes=n_classes, nfeat_name=nfeat_name) if mode == 'regression': Loading
docs/models.rst +9 −0 Original line number Diff line number Diff line Loading @@ -126,6 +126,9 @@ read off what's needed to train the model from the table below. | :code:`GATModel` | Classifier/| :code:`GraphData` | | :code:`MolGraphConvFeaturizer` | :code:`fit` | | | Regressor | | | | | +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ | :code:`GCNModel` | Classifier/| :code:`GraphData` | | :code:`CGCNNFeaturizer` | :code:`fit` | | | Regressor | | | | | +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ Model ----- Loading Loading @@ -447,3 +450,9 @@ GATModel .. autoclass:: deepchem.models.GATModel :members: GCNModel -------- .. autoclass:: deepchem.models.GCNModel :members:
