Loading deepchem/models/torch_models/attentivefp.py +2 −1 Original line number Diff line number Diff line Loading @@ -251,7 +251,8 @@ class AttentiveFPModel(TorchModel): (only used when ``mode`` is 'classification'). Default to 2. self_loop: bool Whether to add self loops for the nodes, i.e. edges from nodes to themselves. Default to True. When input graphs have isolated nodes, self loops allow preserving the original feature of them in message passing. Default to True. kwargs This can include any keyword argument of TorchModel. """ Loading deepchem/models/torch_models/gat.py +2 −1 Original line number Diff line number Diff line Loading @@ -308,7 +308,8 @@ class GATModel(TorchModel): (only used when ``mode`` is 'classification'). Default to 2. self_loop: bool Whether to add self loops for the nodes, i.e. edges from nodes to themselves. Default to True. When input graphs have isolated nodes, self loops allow preserving the original feature of them in message passing. Default to True. kwargs This can include any keyword argument of TorchModel. """ Loading deepchem/models/torch_models/gcn.py +2 −1 Original line number Diff line number Diff line Loading @@ -294,7 +294,8 @@ class GCNModel(TorchModel): (only used when ``mode`` is 'classification'). Default to 2. self_loop: bool Whether to add self loops for the nodes, i.e. edges from nodes to themselves. Default to True. When input graphs have isolated nodes, self loops allow preserving the original feature of them in message passing. Default to True. kwargs This can include any keyword argument of TorchModel. """ Loading deepchem/models/torch_models/mpnn.py +2 −2 Original line number Diff line number Diff line Loading @@ -222,7 +222,7 @@ class MPNNModel(TorchModel): number_atom_features: int = 30, number_bond_features: int = 11, n_classes: int = 2, self_loop: bool = True, self_loop: bool = False, **kwargs): """ Parameters Loading Loading @@ -250,7 +250,7 @@ class MPNNModel(TorchModel): (only used when ``mode`` is 'classification'). Default to 2. self_loop: bool Whether to add self loops for the nodes, i.e. edges from nodes to themselves. Default to True. Generally, an MPNNModel does not require self loops. Default to False. kwargs This can include any keyword argument of TorchModel. """ Loading docs/source/api_reference/models.rst +3 −0 Original line number Diff line number Diff line Loading @@ -465,3 +465,6 @@ MPNNModel .. autoclass:: deepchem.models.torch_models.MPNNModel :members: Note that this is an alternative implementation for MPNN and currently you can only import it from ``deepchem.models.torch_models``. Loading
deepchem/models/torch_models/attentivefp.py +2 −1 Original line number Diff line number Diff line Loading @@ -251,7 +251,8 @@ class AttentiveFPModel(TorchModel): (only used when ``mode`` is 'classification'). Default to 2. self_loop: bool Whether to add self loops for the nodes, i.e. edges from nodes to themselves. Default to True. When input graphs have isolated nodes, self loops allow preserving the original feature of them in message passing. Default to True. kwargs This can include any keyword argument of TorchModel. """ Loading
deepchem/models/torch_models/gat.py +2 −1 Original line number Diff line number Diff line Loading @@ -308,7 +308,8 @@ class GATModel(TorchModel): (only used when ``mode`` is 'classification'). Default to 2. self_loop: bool Whether to add self loops for the nodes, i.e. edges from nodes to themselves. Default to True. When input graphs have isolated nodes, self loops allow preserving the original feature of them in message passing. Default to True. kwargs This can include any keyword argument of TorchModel. """ Loading
deepchem/models/torch_models/gcn.py +2 −1 Original line number Diff line number Diff line Loading @@ -294,7 +294,8 @@ class GCNModel(TorchModel): (only used when ``mode`` is 'classification'). Default to 2. self_loop: bool Whether to add self loops for the nodes, i.e. edges from nodes to themselves. Default to True. When input graphs have isolated nodes, self loops allow preserving the original feature of them in message passing. Default to True. kwargs This can include any keyword argument of TorchModel. """ Loading
deepchem/models/torch_models/mpnn.py +2 −2 Original line number Diff line number Diff line Loading @@ -222,7 +222,7 @@ class MPNNModel(TorchModel): number_atom_features: int = 30, number_bond_features: int = 11, n_classes: int = 2, self_loop: bool = True, self_loop: bool = False, **kwargs): """ Parameters Loading Loading @@ -250,7 +250,7 @@ class MPNNModel(TorchModel): (only used when ``mode`` is 'classification'). Default to 2. self_loop: bool Whether to add self loops for the nodes, i.e. edges from nodes to themselves. Default to True. Generally, an MPNNModel does not require self loops. Default to False. kwargs This can include any keyword argument of TorchModel. """ Loading
docs/source/api_reference/models.rst +3 −0 Original line number Diff line number Diff line Loading @@ -465,3 +465,6 @@ MPNNModel .. autoclass:: deepchem.models.torch_models.MPNNModel :members: Note that this is an alternative implementation for MPNN and currently you can only import it from ``deepchem.models.torch_models``.
