Loading deepchem/feat/base_classes.py +2 −2 Original line number Diff line number Diff line Loading @@ -91,7 +91,7 @@ class Featurizer(object): >>> dc.feat.CircularFingerprint(size=1024, radius=4) CircularFingerprint[radius=4, size=1024, chiral=False, bonds=True, features=False, sparse=False, smiles=False] >>> dc.feat.CGCNNFeaturizer() CGCNNFeaturizer[radius=8.0, max_neighbors=8, step=0.2] CGCNNFeaturizer[radius=8.0, max_neighbors=12, step=0.2] """ args_spec = inspect.getfullargspec(self.__init__) # type: ignore args_names = [arg for arg in args_spec.args if arg != 'self'] Loading Loading @@ -277,7 +277,7 @@ class MolecularFeaturizer(Featurizer): features.append(self._featurize(mol)) except: logger.warning( "Failed to featurize datapoint %d. Appending empty array") "Failed to featurize datapoint %d. Appending empty array", i) features.append(np.array([])) features = np.asarray(features) Loading deepchem/feat/material_featurizers/cgcnn_featurizer.py +2 −2 Original line number Diff line number Diff line Loading @@ -50,14 +50,14 @@ class CGCNNFeaturizer(MaterialStructureFeaturizer): def __init__(self, radius: float = 8.0, max_neighbors: float = 8, max_neighbors: float = 12, step: float = 0.2): """ Parameters ---------- radius: float (default 8.0) Radius of sphere for finding neighbors of atoms in unit cell. max_neighbors: int (default 8) max_neighbors: int (default 12) Maximum number of neighbors to consider when constructing graph. step: float (default 0.2) Step size for Gaussian filter. This value is used when building edge features. Loading deepchem/models/losses.py +11 −1 Original line number Diff line number Diff line Loading @@ -192,7 +192,17 @@ class SparseSoftmaxCrossEntropy(Loss): def _create_pytorch_loss(self): import torch return torch.nn.CrossEntropyLoss(reduction='none') ce_loss = torch.nn.CrossEntropyLoss(reduction='none') def loss(output, labels): # Convert (batch_size, tasks, classes) to (batch_size, classes, tasks) # CrossEntropyLoss only supports (batch_size, classes, tasks) # This is for API consistency if len(output.shape) == 3: output = output.permute(0, 2, 1) return ce_loss(output, labels.long()) return loss def _make_tf_shapes_consistent(output, labels): Loading deepchem/models/tests/test_gat.py +28 −6 Original line number Diff line number Diff line import unittest from deepchem.feat import MolGraphConvFeaturizer from deepchem.models import GATModel, losses from deepchem.models import GATModel from deepchem.models.tests.test_graph_models import get_dataset try: Loading @@ -14,19 +14,41 @@ except: @unittest.skipIf(not has_pytorch_and_pyg, 'PyTorch and PyTorch Geometric are not installed') def test_gat_classification(): def test_gat_regression(): # load datasets featurizer = MolGraphConvFeaturizer() tasks, dataset, transformers, metric = get_dataset( 'regression', featurizer=featurizer) # initialize models n_tasks = len(tasks) model = GATModel(mode='regression', n_tasks=n_tasks, batch_size=10) # overfit test # GAT's convergence is a little slow model.fit(dataset, nb_epoch=300) scores = model.evaluate(dataset, [metric], transformers) assert scores['mean_absolute_error'] < 0.5 @unittest.skipIf(not has_pytorch_and_pyg, 'PyTorch and PyTorch Geometric are not installed') def test_gat_classification(): # load datasets featurizer = MolGraphConvFeaturizer() tasks, dataset, transformers, metric = get_dataset( 'classification', featurizer=featurizer) # initialize models n_tasks = len(tasks) model = GATModel( n_tasks=n_tasks, loss=losses.L2Loss(), batch_size=4, learning_rate=0.001) mode='classification', n_tasks=n_tasks, batch_size=10, learning_rate=0.001) # overfit test model.fit(dataset, nb_epoch=100) # GAT's convergence is a little slow model.fit(dataset, nb_epoch=150) scores = model.evaluate(dataset, [metric], transformers) # TODO: check this asseration is correct or not assert scores['mean_absolute_error'] < 1.0 assert scores['mean-roc_auc_score'] >= 0.9 deepchem/models/torch_models/cgcnn.py +36 −25 Original line number Diff line number Diff line Loading @@ -32,9 +32,9 @@ class CGCNNLayer(nn.Module): >>> print(type(cgcnn_dgl_graph)) <class 'dgl.heterograph.DGLHeteroGraph'> >>> layer = CGCNNLayer(hidden_node_dim=92, edge_dim=41) >>> update_graph = layer(cgcnn_dgl_graph) >>> print(type(update_graph)) <class 'dgl.heterograph.DGLHeteroGraph'> >>> node_feats = cgcnn_dgl_graph.ndata.pop('x') >>> edge_feats = cgcnn_dgl_graph.edata.pop('edge_attr') >>> new_node_feats, new_edge_feats = layer(cgcnn_dgl_graph, node_feats, edge_feats) Notes ----- Loading @@ -57,40 +57,48 @@ class CGCNNLayer(nn.Module): """ super(CGCNNLayer, self).__init__() z_dim = 2 * hidden_node_dim + edge_dim self.linear_with_sigmoid = nn.Linear(z_dim, hidden_node_dim) self.linear_with_softplus = nn.Linear(z_dim, hidden_node_dim) self.batch_norm = nn.BatchNorm1d(hidden_node_dim) if batch_norm else None liner_out_dim = 2 * hidden_node_dim self.linear = nn.Linear(z_dim, liner_out_dim) self.batch_norm = nn.BatchNorm1d(liner_out_dim) if batch_norm else None def message_func(self, edges): z = torch.cat( [edges.src['x'], edges.dst['x'], edges.data['edge_attr']], dim=1) gated_z = torch.sigmoid(self.linear_with_sigmoid(z)) message_z = F.softplus(self.linear_with_softplus(z)) return {'gated_z': gated_z, 'message_z': message_z} z = self.linear(z) if self.batch_norm is not None: z = self.batch_norm(z) gated_z, message_z = z.chunk(2, dim=1) gated_z = torch.sigmoid(gated_z) message_z = F.softplus(message_z) return {'message': gated_z * message_z} def reduce_func(self, nodes): new_h = nodes.data['x'] + torch.sum( nodes.mailbox['gated_z'] * nodes.mailbox['message_z'], dim=1) return {'x': new_h} nbr_sumed = torch.sum(nodes.mailbox['message'], dim=1) new_x = F.softplus(nodes.data['x'] + nbr_sumed) return {'new_x': new_x} def forward(self, dgl_graph): """Update node representaions. def forward(self, dgl_graph, node_feats, edge_feats): """Update node representations. Parameters ---------- dgl_graph: DGLGraph DGLGraph for a batch of graphs. The graph expects that the node features are stored in `ndata['x']`, and the edge features are stored in `edata['edge_attr']`. DGLGraph for a batch of graphs. node_feats: torch.Tensor The node features. The shape is `(N, hidden_node_dim)`. edge_feats: torch.Tensor The edge features. The shape is `(N, hidden_node_dim)`. Returns ------- dgl_graph: DGLGraph DGLGraph for a batch of updated graphs. node_feats: torch.Tensor The updated node features. The shape is `(N, hidden_node_dim)`. """ dgl_graph.ndata['x'] = node_feats dgl_graph.edata['edge_attr'] = edge_feats dgl_graph.update_all(self.message_func, self.reduce_func) if self.batch_norm is not None: dgl_graph.ndata['x'] = self.batch_norm(dgl_graph.ndata['x']) return dgl_graph node_feats = dgl_graph.ndata.pop('new_x') return node_feats class CGCNN(nn.Module): Loading Loading @@ -215,15 +223,18 @@ class CGCNN(nn.Module): """ graph = dgl_graph # embedding node features graph.ndata['x'] = self.embedding(graph.ndata['x']) node_feats = graph.ndata.pop('x') edge_feats = graph.edata.pop('edge_attr') node_feats = self.embedding(node_feats) # convolutional layer for conv in self.conv_layers: graph = conv(graph) node_feats = conv(graph, node_feats, edge_feats) # pooling graph_feat = self.pooling(graph, 'x') graph_feat = self.fc(graph_feat) graph.ndata['updated_x'] = node_feats graph_feat = F.softplus(self.pooling(graph, 'updated_x')) graph_feat = F.softplus(self.fc(graph_feat)) out = self.out(graph_feat) if self.mode == 'regression': Loading Loading
deepchem/feat/base_classes.py +2 −2 Original line number Diff line number Diff line Loading @@ -91,7 +91,7 @@ class Featurizer(object): >>> dc.feat.CircularFingerprint(size=1024, radius=4) CircularFingerprint[radius=4, size=1024, chiral=False, bonds=True, features=False, sparse=False, smiles=False] >>> dc.feat.CGCNNFeaturizer() CGCNNFeaturizer[radius=8.0, max_neighbors=8, step=0.2] CGCNNFeaturizer[radius=8.0, max_neighbors=12, step=0.2] """ args_spec = inspect.getfullargspec(self.__init__) # type: ignore args_names = [arg for arg in args_spec.args if arg != 'self'] Loading Loading @@ -277,7 +277,7 @@ class MolecularFeaturizer(Featurizer): features.append(self._featurize(mol)) except: logger.warning( "Failed to featurize datapoint %d. Appending empty array") "Failed to featurize datapoint %d. Appending empty array", i) features.append(np.array([])) features = np.asarray(features) Loading
deepchem/feat/material_featurizers/cgcnn_featurizer.py +2 −2 Original line number Diff line number Diff line Loading @@ -50,14 +50,14 @@ class CGCNNFeaturizer(MaterialStructureFeaturizer): def __init__(self, radius: float = 8.0, max_neighbors: float = 8, max_neighbors: float = 12, step: float = 0.2): """ Parameters ---------- radius: float (default 8.0) Radius of sphere for finding neighbors of atoms in unit cell. max_neighbors: int (default 8) max_neighbors: int (default 12) Maximum number of neighbors to consider when constructing graph. step: float (default 0.2) Step size for Gaussian filter. This value is used when building edge features. Loading
deepchem/models/losses.py +11 −1 Original line number Diff line number Diff line Loading @@ -192,7 +192,17 @@ class SparseSoftmaxCrossEntropy(Loss): def _create_pytorch_loss(self): import torch return torch.nn.CrossEntropyLoss(reduction='none') ce_loss = torch.nn.CrossEntropyLoss(reduction='none') def loss(output, labels): # Convert (batch_size, tasks, classes) to (batch_size, classes, tasks) # CrossEntropyLoss only supports (batch_size, classes, tasks) # This is for API consistency if len(output.shape) == 3: output = output.permute(0, 2, 1) return ce_loss(output, labels.long()) return loss def _make_tf_shapes_consistent(output, labels): Loading
deepchem/models/tests/test_gat.py +28 −6 Original line number Diff line number Diff line import unittest from deepchem.feat import MolGraphConvFeaturizer from deepchem.models import GATModel, losses from deepchem.models import GATModel from deepchem.models.tests.test_graph_models import get_dataset try: Loading @@ -14,19 +14,41 @@ except: @unittest.skipIf(not has_pytorch_and_pyg, 'PyTorch and PyTorch Geometric are not installed') def test_gat_classification(): def test_gat_regression(): # load datasets featurizer = MolGraphConvFeaturizer() tasks, dataset, transformers, metric = get_dataset( 'regression', featurizer=featurizer) # initialize models n_tasks = len(tasks) model = GATModel(mode='regression', n_tasks=n_tasks, batch_size=10) # overfit test # GAT's convergence is a little slow model.fit(dataset, nb_epoch=300) scores = model.evaluate(dataset, [metric], transformers) assert scores['mean_absolute_error'] < 0.5 @unittest.skipIf(not has_pytorch_and_pyg, 'PyTorch and PyTorch Geometric are not installed') def test_gat_classification(): # load datasets featurizer = MolGraphConvFeaturizer() tasks, dataset, transformers, metric = get_dataset( 'classification', featurizer=featurizer) # initialize models n_tasks = len(tasks) model = GATModel( n_tasks=n_tasks, loss=losses.L2Loss(), batch_size=4, learning_rate=0.001) mode='classification', n_tasks=n_tasks, batch_size=10, learning_rate=0.001) # overfit test model.fit(dataset, nb_epoch=100) # GAT's convergence is a little slow model.fit(dataset, nb_epoch=150) scores = model.evaluate(dataset, [metric], transformers) # TODO: check this asseration is correct or not assert scores['mean_absolute_error'] < 1.0 assert scores['mean-roc_auc_score'] >= 0.9
deepchem/models/torch_models/cgcnn.py +36 −25 Original line number Diff line number Diff line Loading @@ -32,9 +32,9 @@ class CGCNNLayer(nn.Module): >>> print(type(cgcnn_dgl_graph)) <class 'dgl.heterograph.DGLHeteroGraph'> >>> layer = CGCNNLayer(hidden_node_dim=92, edge_dim=41) >>> update_graph = layer(cgcnn_dgl_graph) >>> print(type(update_graph)) <class 'dgl.heterograph.DGLHeteroGraph'> >>> node_feats = cgcnn_dgl_graph.ndata.pop('x') >>> edge_feats = cgcnn_dgl_graph.edata.pop('edge_attr') >>> new_node_feats, new_edge_feats = layer(cgcnn_dgl_graph, node_feats, edge_feats) Notes ----- Loading @@ -57,40 +57,48 @@ class CGCNNLayer(nn.Module): """ super(CGCNNLayer, self).__init__() z_dim = 2 * hidden_node_dim + edge_dim self.linear_with_sigmoid = nn.Linear(z_dim, hidden_node_dim) self.linear_with_softplus = nn.Linear(z_dim, hidden_node_dim) self.batch_norm = nn.BatchNorm1d(hidden_node_dim) if batch_norm else None liner_out_dim = 2 * hidden_node_dim self.linear = nn.Linear(z_dim, liner_out_dim) self.batch_norm = nn.BatchNorm1d(liner_out_dim) if batch_norm else None def message_func(self, edges): z = torch.cat( [edges.src['x'], edges.dst['x'], edges.data['edge_attr']], dim=1) gated_z = torch.sigmoid(self.linear_with_sigmoid(z)) message_z = F.softplus(self.linear_with_softplus(z)) return {'gated_z': gated_z, 'message_z': message_z} z = self.linear(z) if self.batch_norm is not None: z = self.batch_norm(z) gated_z, message_z = z.chunk(2, dim=1) gated_z = torch.sigmoid(gated_z) message_z = F.softplus(message_z) return {'message': gated_z * message_z} def reduce_func(self, nodes): new_h = nodes.data['x'] + torch.sum( nodes.mailbox['gated_z'] * nodes.mailbox['message_z'], dim=1) return {'x': new_h} nbr_sumed = torch.sum(nodes.mailbox['message'], dim=1) new_x = F.softplus(nodes.data['x'] + nbr_sumed) return {'new_x': new_x} def forward(self, dgl_graph): """Update node representaions. def forward(self, dgl_graph, node_feats, edge_feats): """Update node representations. Parameters ---------- dgl_graph: DGLGraph DGLGraph for a batch of graphs. The graph expects that the node features are stored in `ndata['x']`, and the edge features are stored in `edata['edge_attr']`. DGLGraph for a batch of graphs. node_feats: torch.Tensor The node features. The shape is `(N, hidden_node_dim)`. edge_feats: torch.Tensor The edge features. The shape is `(N, hidden_node_dim)`. Returns ------- dgl_graph: DGLGraph DGLGraph for a batch of updated graphs. node_feats: torch.Tensor The updated node features. The shape is `(N, hidden_node_dim)`. """ dgl_graph.ndata['x'] = node_feats dgl_graph.edata['edge_attr'] = edge_feats dgl_graph.update_all(self.message_func, self.reduce_func) if self.batch_norm is not None: dgl_graph.ndata['x'] = self.batch_norm(dgl_graph.ndata['x']) return dgl_graph node_feats = dgl_graph.ndata.pop('new_x') return node_feats class CGCNN(nn.Module): Loading Loading @@ -215,15 +223,18 @@ class CGCNN(nn.Module): """ graph = dgl_graph # embedding node features graph.ndata['x'] = self.embedding(graph.ndata['x']) node_feats = graph.ndata.pop('x') edge_feats = graph.edata.pop('edge_attr') node_feats = self.embedding(node_feats) # convolutional layer for conv in self.conv_layers: graph = conv(graph) node_feats = conv(graph, node_feats, edge_feats) # pooling graph_feat = self.pooling(graph, 'x') graph_feat = self.fc(graph_feat) graph.ndata['updated_x'] = node_feats graph_feat = F.softplus(self.pooling(graph, 'updated_x')) graph_feat = F.softplus(self.fc(graph_feat)) out = self.out(graph_feat) if self.mode == 'regression': Loading
