/
attentivefp.py
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/
attentivefp.py
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"""
DGL-based AttentiveFP for graph property prediction.
"""
import torch.nn as nn
import torch.nn.functional as F
from deepchem.models.losses import Loss, L2Loss, SparseSoftmaxCrossEntropy
from deepchem.models.torch_models.torch_model import TorchModel
class AttentiveFP(nn.Module):
"""Model for Graph Property Prediction.
This model proceeds as follows:
* Combine node features and edge features for initializing node representations,
which involves a round of message passing
* Update node representations with multiple rounds of message passing
* For each graph, compute its representation by combining the representations
of all nodes in it, which involves a gated recurrent unit (GRU).
* Perform the final prediction using a linear layer
Examples
--------
>>> import deepchem as dc
>>> import dgl
>>> from deepchem.models import AttentiveFP
>>> 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(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')
>>> preds = model(batch_dgl_graph)
>>> print(type(preds))
<class 'torch.Tensor'>
>>> preds.shape == (2, 1)
True
References
----------
.. [1] Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li,
Zhaojun Li, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, and Mingyue Zheng. "Pushing
the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention
Mechanism." Journal of Medicinal Chemistry. 2020, 63, 16, 8749–8760.
Notes
-----
This class requires DGL (https://github.com/dmlc/dgl) and DGL-LifeSci
(https://github.com/awslabs/dgl-lifesci) to be installed.
"""
def __init__(self,
n_tasks: int,
num_layers: int = 2,
num_timesteps: int = 2,
graph_feat_size: int = 200,
dropout: float = 0.,
mode: str = 'regression',
number_atom_features: int = 30,
number_bond_features: int = 11,
n_classes: int = 2,
nfeat_name: str = 'x',
efeat_name: str = 'edge_attr'):
"""
Parameters
----------
n_tasks: int
Number of tasks.
num_layers: int
Number of graph neural network layers, i.e. number of rounds of message passing.
Default to 2.
num_timesteps: int
Number of time steps for updating graph representations with a GRU. Default to 2.
graph_feat_size: int
Size for graph representations. Default to 200.
dropout: float
Dropout probability. Default to 0.
mode: str
The model type, 'classification' or 'regression'. Default to 'regression'.
number_atom_features: int
The length of the initial atom feature vectors. Default to 30.
number_bond_features: int
The length of the initial bond feature vectors. Default to 11.
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'.
"""
try:
import dgl # noqa: F401
except:
raise ImportError('This class requires dgl.')
try:
import dgllife # noqa: F401
except:
raise ImportError('This class requires dgllife.')
if mode not in ['classification', 'regression']:
raise ValueError(
"mode must be either 'classification' or 'regression'")
super(AttentiveFP, self).__init__()
self.n_tasks = n_tasks
self.mode = mode
self.n_classes = n_classes
self.nfeat_name = nfeat_name
self.efeat_name = efeat_name
if mode == 'classification':
out_size = n_tasks * n_classes
else:
out_size = n_tasks
from dgllife.model import AttentiveFPPredictor as DGLAttentiveFPPredictor
self.model = DGLAttentiveFPPredictor(
node_feat_size=number_atom_features,
edge_feat_size=number_bond_features,
num_layers=num_layers,
num_timesteps=num_timesteps,
graph_feat_size=graph_feat_size,
n_tasks=out_size,
dropout=dropout)
def forward(self, g):
"""Predict graph labels
Parameters
----------
g: DGLGraph
A DGLGraph for a batch of graphs. It stores the node features in
``dgl_graph.ndata[self.nfeat_name]`` and edge features in
``dgl_graph.edata[self.efeat_name]``.
Returns
-------
torch.Tensor
The model output.
* When self.mode = 'regression',
its shape will be ``(dgl_graph.batch_size, self.n_tasks)``.
* When self.mode = 'classification', the output consists of probabilities
for classes. Its shape will be
``(dgl_graph.batch_size, self.n_tasks, self.n_classes)`` if self.n_tasks > 1;
its shape will be ``(dgl_graph.batch_size, self.n_classes)`` if self.n_tasks is 1.
torch.Tensor, optional
This is only returned when self.mode = 'classification', the output consists of the
logits for classes before softmax.
"""
node_feats = g.ndata[self.nfeat_name]
edge_feats = g.edata[self.efeat_name]
out = self.model(g, node_feats, edge_feats)
if self.mode == 'classification':
if self.n_tasks == 1:
logits = out.view(-1, self.n_classes)
softmax_dim = 1
else:
logits = out.view(-1, self.n_tasks, self.n_classes)
softmax_dim = 2
proba = F.softmax(logits, dim=softmax_dim)
return proba, logits
else:
return out
class AttentiveFPModel(TorchModel):
"""Model for Graph Property Prediction.
This model proceeds as follows:
* Combine node features and edge features for initializing node representations,
which involves a round of message passing
* Update node representations with multiple rounds of message passing
* For each graph, compute its representation by combining the representations
of all nodes in it, which involves a gated recurrent unit (GRU).
* Perform the final prediction using a linear layer
Examples
--------
>>> import deepchem as dc
>>> from deepchem.models import AttentiveFPModel
>>> # preparing dataset
>>> smiles = ["C1CCC1", "C1=CC=CN=C1"]
>>> labels = [0., 1.]
>>> featurizer = dc.feat.MolGraphConvFeaturizer(use_edges=True)
>>> X = featurizer.featurize(smiles)
>>> dataset = dc.data.NumpyDataset(X=X, y=labels)
>>> # training model
>>> model = AttentiveFPModel(mode='classification', n_tasks=1,
... batch_size=16, learning_rate=0.001)
>>> loss = model.fit(dataset, nb_epoch=5)
References
----------
.. [1] Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li,
Zhaojun Li, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, and Mingyue Zheng. "Pushing
the Boundaries of Molecular Representation for Drug Discovery with the Graph
Attention Mechanism." Journal of Medicinal Chemistry. 2020, 63, 16, 8749–8760.
Notes
-----
This class requires DGL (https://github.com/dmlc/dgl) and DGL-LifeSci
(https://github.com/awslabs/dgl-lifesci) to be installed.
"""
def __init__(self,
n_tasks: int,
num_layers: int = 2,
num_timesteps: int = 2,
graph_feat_size: int = 200,
dropout: float = 0.,
mode: str = 'regression',
number_atom_features: int = 30,
number_bond_features: int = 11,
n_classes: int = 2,
self_loop: bool = True,
**kwargs):
"""
Parameters
----------
n_tasks: int
Number of tasks.
num_layers: int
Number of graph neural network layers, i.e. number of rounds of message passing.
Default to 2.
num_timesteps: int
Number of time steps for updating graph representations with a GRU. Default to 2.
graph_feat_size: int
Size for graph representations. Default to 200.
dropout: float
Dropout probability. Default to 0.
mode: str
The model type, 'classification' or 'regression'. Default to 'regression'.
number_atom_features: int
The length of the initial atom feature vectors. Default to 30.
number_bond_features: int
The length of the initial bond feature vectors. Default to 11.
n_classes: int
The number of classes to predict per task
(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.
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.
"""
model = AttentiveFP(n_tasks=n_tasks,
num_layers=num_layers,
num_timesteps=num_timesteps,
graph_feat_size=graph_feat_size,
dropout=dropout,
mode=mode,
number_atom_features=number_atom_features,
number_bond_features=number_bond_features,
n_classes=n_classes)
if mode == 'regression':
loss: Loss = L2Loss()
output_types = ['prediction']
else:
loss = SparseSoftmaxCrossEntropy()
output_types = ['prediction', 'loss']
super(AttentiveFPModel, self).__init__(model,
loss=loss,
output_types=output_types,
**kwargs)
self._self_loop = self_loop
def _prepare_batch(self, batch):
"""Create batch data for AttentiveFP.
Parameters
----------
batch: tuple
The tuple is ``(inputs, labels, weights)``.
Returns
-------
inputs: DGLGraph
DGLGraph for a batch of graphs.
labels: list of torch.Tensor or None
The graph labels.
weights: list of torch.Tensor or None
The weights for each sample or sample/task pair converted to torch.Tensor.
"""
try:
import dgl
except:
raise ImportError('This class requires dgl.')
inputs, labels, weights = batch
dgl_graphs = [
graph.to_dgl_graph(self_loop=self._self_loop) for graph in inputs[0]
]
inputs = dgl.batch(dgl_graphs).to(self.device)
_, labels, weights = super(AttentiveFPModel, self)._prepare_batch(
([], labels, weights))
return inputs, labels, weights