Update (bdc73970) · Commits · 钟慕尧 / deepchem

deepchem/models/torch_models/attentivefp.py

+1 −13

Original line number	Diff line number	Diff line
		@@ -224,8 +224,6 @@ class AttentiveFPModel(TorchModel):
		number_atom_features: int = 30,
		number_bond_features: int = 11,
		n_classes: int = 2,
		nfeat_name: str = 'x',
		efeat_name: str = 'edge_attr',
		self_loop: bool = True,
		**kwargs):
		"""
		@@ -251,14 +249,6 @@ class AttentiveFPModel(TorchModel):
		n_classes: int
		The number of classes to predict per task
		(only used when ``mode`` is 'classification'). Default to 2.
		nfeat_name: str
		For an input graph ``g``, the model assumes that it stores node features in
		``g.ndata[nfeat_name]`` and will retrieve input node features from that.
		Default to 'x'.
		efeat_name: str
		For an input graph ``g``, the model assumes that it stores edge features in
		``g.edata[efeat_name]`` and will retrieve input edge features from that.
		Default to 'edge_attr'.
		self_loop: bool
		Whether to add self loops for the nodes, i.e. edges from nodes to themselves.
		Default to True.
		@@ -274,9 +264,7 @@ class AttentiveFPModel(TorchModel):
		mode=mode,
		number_atom_features=number_atom_features,
		number_bond_features=number_bond_features,
		n_classes=n_classes,
		nfeat_name=nfeat_name,
		efeat_name=efeat_name)
		n_classes=n_classes)
		if mode == 'regression':
		loss: Loss = L2Loss()
		output_types = ['prediction']

+1 −7

Original line number	Diff line number	Diff line
		@@ -264,7 +264,6 @@ class GATModel(TorchModel):
		mode: str = 'regression',
		number_atom_features: int = 30,
		n_classes: int = 2,
		nfeat_name: str = 'x',
		self_loop: bool = True,
		**kwargs):
		"""
		@@ -307,10 +306,6 @@ class GATModel(TorchModel):
		n_classes: int
		The number of classes to predict per task
		(only used when ``mode`` is 'classification'). Default to 2.
		nfeat_name: str
		For an input graph ``g``, the model assumes that it stores node features in
		``g.ndata[nfeat_name]`` and will retrieve input node features from that.
		Default to 'x'.
		self_loop: bool
		Whether to add self loops for the nodes, i.e. edges from nodes to themselves.
		Default to True.
		@@ -330,8 +325,7 @@ class GATModel(TorchModel):
		predictor_dropout=predictor_dropout,
		mode=mode,
		number_atom_features=number_atom_features,
		n_classes=n_classes,
		nfeat_name=nfeat_name)
		n_classes=n_classes)
		if mode == 'regression':
		loss: Loss = L2Loss()
		output_types = ['prediction']

+1 −7

Original line number	Diff line number	Diff line
		@@ -261,7 +261,6 @@ class GCNModel(TorchModel):
		mode: str = 'regression',
		number_atom_features=30,
		n_classes: int = 2,
		nfeat_name: str = 'x',
		self_loop: bool = True,
		**kwargs):
		"""
		@@ -293,10 +292,6 @@ class GCNModel(TorchModel):
		n_classes: int
		The number of classes to predict per task
		(only used when ``mode`` is 'classification'). Default to 2.
		nfeat_name: str
		For an input graph ``g``, the model assumes that it stores node features in
		``g.ndata[nfeat_name]`` and will retrieve input node features from that.
		Default to 'x'.
		self_loop: bool
		Whether to add self loops for the nodes, i.e. edges from nodes to themselves.
		Default to True.
		@@ -314,8 +309,7 @@ class GCNModel(TorchModel):
		predictor_dropout=predictor_dropout,
		mode=mode,
		number_atom_features=number_atom_features,
		n_classes=n_classes,
		nfeat_name=nfeat_name)
		n_classes=n_classes)
		if mode == 'regression':
		loss: Loss = L2Loss()
		output_types = ['prediction']

+1 −13

Original line number	Diff line number	Diff line
		@@ -222,8 +222,6 @@ class MPNNModel(TorchModel):
		number_atom_features: int = 30,
		number_bond_features: int = 11,
		n_classes: int = 2,
		nfeat_name: str = 'x',
		efeat_name: str = 'edge_attr',
		self_loop: bool = True,
		**kwargs):
		"""
		@@ -250,14 +248,6 @@ class MPNNModel(TorchModel):
		n_classes: int
		The number of classes to predict per task
		(only used when ``mode`` is 'classification'). Default to 2.
		nfeat_name: str
		For an input graph ``g``, the model assumes that it stores node features in
		``g.ndata[nfeat_name]`` and will retrieve input node features from that.
		Default to 'x'.
		efeat_name: str
		For an input graph ``g``, the model assumes that it stores edge features in
		``g.edata[efeat_name]`` and will retrieve input edge features from that.
		Default to 'edge_attr'.
		self_loop: bool
		Whether to add self loops for the nodes, i.e. edges from nodes to themselves.
		Default to True.
		@@ -274,9 +264,7 @@ class MPNNModel(TorchModel):
		mode=mode,
		number_atom_features=number_atom_features,
		number_bond_features=number_bond_features,
		n_classes=n_classes,
		nfeat_name=nfeat_name,
		efeat_name=efeat_name)
		n_classes=n_classes)
		if mode == 'regression':
		loss: Loss = L2Loss()
		output_types = ['prediction']