Update (cb205179) · Commits · 钟慕尧 / deepchem

deepchem/models/torch_models/attentivefp.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -31,7 +31,7 @@ class AttentiveFP(nn.Module):
		>>> graphs = featurizer.featurize(smiles)
		>>> print(type(graphs[0]))
		<class 'deepchem.feat.graph_data.GraphData'>
		>>> dgl_graphs = [graphs[i].to_dgl_graph() for i in range(len(graphs))]
		>>> dgl_graphs = [graphs[i].to_dgl_graph(self_loop=True) for i in range(len(graphs))]
		>>> # Batch two graphs into a graph of two connected components
		>>> batch_dgl_graph = dgl.batch(dgl_graphs)
		>>> model = AttentiveFP(n_tasks=1, mode='regression')

deepchem/models/torch_models/gat.py

+1 −4

Original line number	Diff line number	Diff line
		@@ -31,7 +31,7 @@ class GAT(nn.Module):
		>>> graphs = featurizer.featurize(smiles)
		>>> print(type(graphs[0]))
		<class 'deepchem.feat.graph_data.GraphData'>
		>>> dgl_graphs = [graphs[i].to_dgl_graph() for i in range(len(graphs))]
		>>> dgl_graphs = [graphs[i].to_dgl_graph(self_loop=True) for i in range(len(graphs))]
		>>> # Batch two graphs into a graph of two connected components
		>>> batch_dgl_graph = dgl.batch(dgl_graphs)
		>>> model = GAT(n_tasks=1, mode='regression')
		@@ -350,9 +350,6 @@ class GATModel(TorchModel):
		----------
		batch: tuple
		The tuple is ``(inputs, labels, weights)``.
		self_loop: bool
		Whether to add self loops for the nodes, i.e. edges from nodes
		to themselves. Default to False.

		Returns
		-------

deepchem/models/torch_models/gcn.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -31,7 +31,7 @@ class GCN(nn.Module):
		>>> graphs = featurizer.featurize(smiles)
		>>> print(type(graphs[0]))
		<class 'deepchem.feat.graph_data.GraphData'>
		>>> dgl_graphs = [graphs[i].to_dgl_graph() for i in range(len(graphs))]
		>>> dgl_graphs = [graphs[i].to_dgl_graph(self_loop=True) for i in range(len(graphs))]
		>>> # Batch two graphs into a graph of two connected components
		>>> batch_dgl_graph = dgl.batch(dgl_graphs)
		>>> model = GCN(n_tasks=1, mode='regression')

deepchem/models/torch_models/mpnn.py

+32 −3

Original line number	Diff line number	Diff line
		@@ -24,7 +24,21 @@ class MPNN(nn.Module):

		>>> import deepchem as dc
		>>> import dgl
		TODO
		>>> from deepchem.models.torch_models import MPNN
		>>> smiles = ["C1CCC1", "C1=CC=CN=C1"]
		>>> featurizer = dc.feat.MolGraphConvFeaturizer(use_edges=True)
		>>> graphs = featurizer.featurize(smiles)
		>>> print(type(graphs[0]))
		<class 'deepchem.feat.graph_data.GraphData'>
		>>> dgl_graphs = [graphs[i].to_dgl_graph() for i in range(len(graphs))]
		>>> # Batch two graphs into a graph of two connected components
		>>> batch_dgl_graph = dgl.batch(dgl_graphs)
		>>> model = MPNN(n_tasks=1, mode='regression')
		>>> preds = model(batch_dgl_graph)
		>>> print(type(preds))
		<class 'torch.Tensor'>
		>>> preds.shape == (2, 1)
		True

		References
		----------
		@@ -117,8 +131,7 @@ class MPNN(nn.Module):
		n_tasks=out_size,
		num_step_message_passing=num_step_message_passing,
		num_step_set2set=num_step_set2set,
		num_layer_set2set=num_layer_set2set
		)
		num_layer_set2set=num_layer_set2set)

		def forward(self, g):
		"""Predict graph labels
		@@ -160,3 +173,19 @@ class MPNN(nn.Module):
		return proba, logits
		else:
		return out


		class MPNNModel(nn.Module):
		"""Model for graph property prediction

		This model proceeds as follows:

		* Combine latest node representations and edge features in updating node representations,
		which involves multiple rounds of message passing
		* For each graph, compute its representation by combining the representations
		of all nodes in it, which involves a Set2Set layer.
		* Perform the final prediction using an MLP

		Examples
		--------
		"""

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