Loading deepchem/models/tf_keras_models/graph_models.py 0 → 100644 +143 −0 Original line number Diff line number Diff line """ Convenience classes for assembling graph models. """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Han Altae-Tran and Bharath Ramsundar" __copyright__ = "Copyright 2016, Stanford University" __license__ = "GPL" from deepchem.models.tf_keras_models.keras_layers import GraphGather from deepchem.models.tf_keras_models.containers import GraphContainer from deepchem.models.tf_keras_models.containers import SupportGraphContainer from deepchem.models.tf_keras_models.graph_topology import GraphTopology class SequentialGraphModel(object): def __init__(self, n_atoms, n_feat, batch_size): #super(SequentialGraphModel, self).__init__() self.batch_size = batch_size # Create graph topology and x self.graph_topology = GraphTopology(n_atoms, n_feat, self.batch_size) self.output = self.graph_topology.get_nodes() self.layers = [] # Keep track of the layers def add(self, layer): """Adds a new layer to model.""" # Update new value of x if type(layer).__name__ in ['GraphConv', 'GraphGather', 'GraphPool']: if (len(self.layers) > 0 and hasattr(self.layers[-1], "__name__")): assert (self.layers[-1].__name__ != "GraphGather", 'Cannot use GraphConv or GraphGather layers after a GraphGather') self.output = layer( [self.output] + self.graph_topology.get_topology_placeholders()) else: self.output = layer(self.output) # Add layer to the layer list self.layers.append(layer) ''' def graph_gather(self, activation='linear'): gather = GraphGather(self.batch_size, activation=activation) self.layers.append(gather) self.output = gather( [self.output] + self.graph_topology.get_topology_placeholders()) ''' def return_container(self, sess): return GraphContainer(sess, input=self.return_inputs(), output=self.return_outputs(), graph_topology=self.graph_topology) def return_outputs(self): return self.output def return_inputs(self): return self.graph_topology.get_input_placeholders() def get_layer(self, layer_id): return self.layers[layer_id] class SequentialSupportGraphModel(object): def __init__(self, n_atom, n_feat, test_batch_size, support_batch_size): self.test_batch_size = test_batch_size self.support_batch_size = support_batch_size # Create graph topology and x self.test_graph_topology = GraphTopology( n_atom, n_feat, test_batch_size, name='test') self.support_graph_topology = GraphTopology( n_atom, n_feat, support_batch_size, name='support') self.test = self.test_graph_topology.get_nodes() self.support = self.support_graph_topology.get_nodes() # Keep track of the layers self.layers = [] # Whether or not we have used the GraphGather layer yet self.bool_pre_gather = True def add(self, layer): # Add layer to the layer list self.layers.append(layer) ############################################################# DEBUG print("SequentialSupportGraphModel.add()") print(layer) ############################################################# DEBUG # Update new value of x if type(layer).__name__ in ['GraphConv', 'GraphGather', 'GraphPool']: assert (self.bool_pre_gather, 'Cannot use GraphConv or GraphGather layers after a GraphGather') self.test = layer([self.test] + self.test_graph_topology.topology) self.support = layer([self.support] + self.support_graph_topology.topology) else: self.test = layer(self.test) self.support = layer(self.support) if type(layer).__name__ == 'GraphGather': self.bool_pre_gather = False # Set flag to stop adding topology def join(self, layer, swap=False): """Joins test and support to a two input two output layer""" self.layers.append(layer) if not swap: self.test, self.support = layer([self.test, self.support]) else: self.support, self.test = layer([self.support, self.test]) def graph_gather(self, activation='linear'): gather1 = GraphGather(self.test_batch_size, activation=activation) gather2 = GraphGather(self.support_batch_size, activation=activation) self.layers.append(gather1) self.layers.append(gather2) self.test = gather1([self.test] + self.test_graph_topology.topology) self.support = gather2([self.support] + self.support_graph_topology.topology) self.bool_pre_gather = False def return_container(self, sess): return SupportGraphContainer( sess, input=self.return_inputs(), output=self.return_outputs(), graph_topology_test=self.test_graph_topology, graph_topology_support=self.support_graph_topology) def return_outputs(self): return [self.test] + [self.support] def return_inputs(self): return self.test_graph_topology.get_inputs() + self.support_graph_topology.get_inputs() def get_layer(self, layer_id): return self.layers[layer_id] deepchem/models/tf_keras_models/graph_topology.py +27 −21 Original line number Diff line number Diff line Loading @@ -52,13 +52,11 @@ class GraphTopology(object): self.name = name self.max_deg = max_deg self.min_deg = min_deg self.init_keras_placeholders() def init_keras_placeholders(self): self.nodes_placeholder = Input( self.atom_features_placeholder = Input( tensor=K.placeholder( shape=(None, self.n_feat), dtype='float32', name=self.name+'_nodes')) name=self.name+'_atom_features')) self.deg_adj_lists_placeholders = [ Input(tensor=K.placeholder( shape=(None, deg), dtype='int32', name=self.name+'_deg_adj'+str(deg))) Loading @@ -74,39 +72,46 @@ class GraphTopology(object): # Define the list of tensors to be used as topology self.topology = [self.deg_slice_placeholder, self.membership_placeholder] self.topology.extend(self.deg_adj_lists_placeholders) self.topology += self.deg_adj_lists_placeholders self.inputs = [self.nodes_placeholder] self.inputs.extend(self.topology) self.inputs = [self.atom_features_placeholder] self.inputs += self.topology def get_inputs(self): def get_input_placeholders(self): """All placeholders. Contains atom_features placeholder and topology placeholders. """ return self.inputs def get_topology(self): def get_topology_placeholders(self): """Returns topology placeholders Consists of deg_slice_placeholder, membership_placeholder, and the deg_adj_list_placeholders. """ return self.topology def get_batch_size(self): return self.batch_size def get_nodes(self): return self.nodes_placeholder def get_atom_features_placeholder(self): return self.atom_features_placeholder def get_deg_adj_lists(self): def get_deg_adjacency_lists_placeholders(self): return self.deg_adj_lists_placeholders def get_deg_slice(self): def get_deg_slice_placeholder(self): return self.deg_slice_placeholder def get_membership(self): def get_membership_placeholder(self): return self.membership_placeholder # TODO(rbharath): It's still not clear to me that this should live alone like # this... It's awkward to separate out part of the batch construction this # way. def batch_to_feed_dict(self, batch): """Converts the current batch into a feed_dict used by tensorflow. """Converts the current batch of mol_graphs into tensorflow feed_dict. Assigns the graph information in batch to the placeholders tensors Assigns the graph information in array of ConvMol objects to the placeholders tensors params ------ Loading @@ -120,8 +125,7 @@ class GraphTopology(object): """ # Merge mol conv objects batch = ConvMol.agglomerate_mols(batch) atoms = batch.nodes atoms = batch.get_atom_features() deg_adj_lists = [batch.deg_adj_lists[deg] for deg in range(1, self.max_deg+1)] Loading @@ -132,6 +136,7 @@ class GraphTopology(object): self.membership_placeholder : batch.membership} return merge_dicts([atoms_dict, deg_adj_dict]) ''' def extract_topology(x): # Extracts the topology tensors from x topology = x[1::] Loading @@ -149,3 +154,4 @@ def extract_nodes(x): def extract_membership(x): return x[2] ''' deepchem/models/tf_keras_models/keras_layers.py 0 → 100644 +681 −0 File added.Preview size limit exceeded, changes collapsed. Show changes deepchem/models/tf_keras_models/tests/test_containers.py +1 −1 Original line number Diff line number Diff line Loading @@ -16,7 +16,7 @@ from deepchem.models.tf_keras_models.graph_topology import GraphTopology class TestContainers(test_util.TensorFlowTestCase): """ Test Container usage." Test Container usage. """ def setUp(self): super(TestContainers, self).setUp() Loading deepchem/models/tf_keras_models/tests/test_graph_models.py 0 → 100644 +32 −0 Original line number Diff line number Diff line """ Testing construction of graph models. """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Han Altae-Tran and Bharath Ramsundar" __copyright__ = "Copyright 2016, Stanford University" __license__ = "GPL" import unittest from tensorflow.python.framework import test_util from deepchem.models.tf_keras_models.containers import GraphContainer from deepchem.models.tf_keras_models.graph_topology import GraphTopology from deepchem.models.tf_keras_models.graph_models import SequentialGraphModel class TestGraphModels(test_util.TensorFlowTestCase): """ Test Container usage. """ def setUp(self): super(TestGraphModels, self).setUp() self.root = '/tmp' def test_sequential_graph_model(self): """Simple test that SequentialGraphModel can be initialized.""" n_atoms = 5 n_atom_feat = 10 batch_size = 3 graph_model = SequentialGraphModel(n_atoms, n_feat, batch_size) assert len(graph_model.layers) == 0 Loading
deepchem/models/tf_keras_models/graph_models.py 0 → 100644 +143 −0 Original line number Diff line number Diff line """ Convenience classes for assembling graph models. """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Han Altae-Tran and Bharath Ramsundar" __copyright__ = "Copyright 2016, Stanford University" __license__ = "GPL" from deepchem.models.tf_keras_models.keras_layers import GraphGather from deepchem.models.tf_keras_models.containers import GraphContainer from deepchem.models.tf_keras_models.containers import SupportGraphContainer from deepchem.models.tf_keras_models.graph_topology import GraphTopology class SequentialGraphModel(object): def __init__(self, n_atoms, n_feat, batch_size): #super(SequentialGraphModel, self).__init__() self.batch_size = batch_size # Create graph topology and x self.graph_topology = GraphTopology(n_atoms, n_feat, self.batch_size) self.output = self.graph_topology.get_nodes() self.layers = [] # Keep track of the layers def add(self, layer): """Adds a new layer to model.""" # Update new value of x if type(layer).__name__ in ['GraphConv', 'GraphGather', 'GraphPool']: if (len(self.layers) > 0 and hasattr(self.layers[-1], "__name__")): assert (self.layers[-1].__name__ != "GraphGather", 'Cannot use GraphConv or GraphGather layers after a GraphGather') self.output = layer( [self.output] + self.graph_topology.get_topology_placeholders()) else: self.output = layer(self.output) # Add layer to the layer list self.layers.append(layer) ''' def graph_gather(self, activation='linear'): gather = GraphGather(self.batch_size, activation=activation) self.layers.append(gather) self.output = gather( [self.output] + self.graph_topology.get_topology_placeholders()) ''' def return_container(self, sess): return GraphContainer(sess, input=self.return_inputs(), output=self.return_outputs(), graph_topology=self.graph_topology) def return_outputs(self): return self.output def return_inputs(self): return self.graph_topology.get_input_placeholders() def get_layer(self, layer_id): return self.layers[layer_id] class SequentialSupportGraphModel(object): def __init__(self, n_atom, n_feat, test_batch_size, support_batch_size): self.test_batch_size = test_batch_size self.support_batch_size = support_batch_size # Create graph topology and x self.test_graph_topology = GraphTopology( n_atom, n_feat, test_batch_size, name='test') self.support_graph_topology = GraphTopology( n_atom, n_feat, support_batch_size, name='support') self.test = self.test_graph_topology.get_nodes() self.support = self.support_graph_topology.get_nodes() # Keep track of the layers self.layers = [] # Whether or not we have used the GraphGather layer yet self.bool_pre_gather = True def add(self, layer): # Add layer to the layer list self.layers.append(layer) ############################################################# DEBUG print("SequentialSupportGraphModel.add()") print(layer) ############################################################# DEBUG # Update new value of x if type(layer).__name__ in ['GraphConv', 'GraphGather', 'GraphPool']: assert (self.bool_pre_gather, 'Cannot use GraphConv or GraphGather layers after a GraphGather') self.test = layer([self.test] + self.test_graph_topology.topology) self.support = layer([self.support] + self.support_graph_topology.topology) else: self.test = layer(self.test) self.support = layer(self.support) if type(layer).__name__ == 'GraphGather': self.bool_pre_gather = False # Set flag to stop adding topology def join(self, layer, swap=False): """Joins test and support to a two input two output layer""" self.layers.append(layer) if not swap: self.test, self.support = layer([self.test, self.support]) else: self.support, self.test = layer([self.support, self.test]) def graph_gather(self, activation='linear'): gather1 = GraphGather(self.test_batch_size, activation=activation) gather2 = GraphGather(self.support_batch_size, activation=activation) self.layers.append(gather1) self.layers.append(gather2) self.test = gather1([self.test] + self.test_graph_topology.topology) self.support = gather2([self.support] + self.support_graph_topology.topology) self.bool_pre_gather = False def return_container(self, sess): return SupportGraphContainer( sess, input=self.return_inputs(), output=self.return_outputs(), graph_topology_test=self.test_graph_topology, graph_topology_support=self.support_graph_topology) def return_outputs(self): return [self.test] + [self.support] def return_inputs(self): return self.test_graph_topology.get_inputs() + self.support_graph_topology.get_inputs() def get_layer(self, layer_id): return self.layers[layer_id]
deepchem/models/tf_keras_models/graph_topology.py +27 −21 Original line number Diff line number Diff line Loading @@ -52,13 +52,11 @@ class GraphTopology(object): self.name = name self.max_deg = max_deg self.min_deg = min_deg self.init_keras_placeholders() def init_keras_placeholders(self): self.nodes_placeholder = Input( self.atom_features_placeholder = Input( tensor=K.placeholder( shape=(None, self.n_feat), dtype='float32', name=self.name+'_nodes')) name=self.name+'_atom_features')) self.deg_adj_lists_placeholders = [ Input(tensor=K.placeholder( shape=(None, deg), dtype='int32', name=self.name+'_deg_adj'+str(deg))) Loading @@ -74,39 +72,46 @@ class GraphTopology(object): # Define the list of tensors to be used as topology self.topology = [self.deg_slice_placeholder, self.membership_placeholder] self.topology.extend(self.deg_adj_lists_placeholders) self.topology += self.deg_adj_lists_placeholders self.inputs = [self.nodes_placeholder] self.inputs.extend(self.topology) self.inputs = [self.atom_features_placeholder] self.inputs += self.topology def get_inputs(self): def get_input_placeholders(self): """All placeholders. Contains atom_features placeholder and topology placeholders. """ return self.inputs def get_topology(self): def get_topology_placeholders(self): """Returns topology placeholders Consists of deg_slice_placeholder, membership_placeholder, and the deg_adj_list_placeholders. """ return self.topology def get_batch_size(self): return self.batch_size def get_nodes(self): return self.nodes_placeholder def get_atom_features_placeholder(self): return self.atom_features_placeholder def get_deg_adj_lists(self): def get_deg_adjacency_lists_placeholders(self): return self.deg_adj_lists_placeholders def get_deg_slice(self): def get_deg_slice_placeholder(self): return self.deg_slice_placeholder def get_membership(self): def get_membership_placeholder(self): return self.membership_placeholder # TODO(rbharath): It's still not clear to me that this should live alone like # this... It's awkward to separate out part of the batch construction this # way. def batch_to_feed_dict(self, batch): """Converts the current batch into a feed_dict used by tensorflow. """Converts the current batch of mol_graphs into tensorflow feed_dict. Assigns the graph information in batch to the placeholders tensors Assigns the graph information in array of ConvMol objects to the placeholders tensors params ------ Loading @@ -120,8 +125,7 @@ class GraphTopology(object): """ # Merge mol conv objects batch = ConvMol.agglomerate_mols(batch) atoms = batch.nodes atoms = batch.get_atom_features() deg_adj_lists = [batch.deg_adj_lists[deg] for deg in range(1, self.max_deg+1)] Loading @@ -132,6 +136,7 @@ class GraphTopology(object): self.membership_placeholder : batch.membership} return merge_dicts([atoms_dict, deg_adj_dict]) ''' def extract_topology(x): # Extracts the topology tensors from x topology = x[1::] Loading @@ -149,3 +154,4 @@ def extract_nodes(x): def extract_membership(x): return x[2] '''
deepchem/models/tf_keras_models/keras_layers.py 0 → 100644 +681 −0 File added.Preview size limit exceeded, changes collapsed. Show changes
deepchem/models/tf_keras_models/tests/test_containers.py +1 −1 Original line number Diff line number Diff line Loading @@ -16,7 +16,7 @@ from deepchem.models.tf_keras_models.graph_topology import GraphTopology class TestContainers(test_util.TensorFlowTestCase): """ Test Container usage." Test Container usage. """ def setUp(self): super(TestContainers, self).setUp() Loading
deepchem/models/tf_keras_models/tests/test_graph_models.py 0 → 100644 +32 −0 Original line number Diff line number Diff line """ Testing construction of graph models. """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Han Altae-Tran and Bharath Ramsundar" __copyright__ = "Copyright 2016, Stanford University" __license__ = "GPL" import unittest from tensorflow.python.framework import test_util from deepchem.models.tf_keras_models.containers import GraphContainer from deepchem.models.tf_keras_models.graph_topology import GraphTopology from deepchem.models.tf_keras_models.graph_models import SequentialGraphModel class TestGraphModels(test_util.TensorFlowTestCase): """ Test Container usage. """ def setUp(self): super(TestGraphModels, self).setUp() self.root = '/tmp' def test_sequential_graph_model(self): """Simple test that SequentialGraphModel can be initialized.""" n_atoms = 5 n_atom_feat = 10 batch_size = 3 graph_model = SequentialGraphModel(n_atoms, n_feat, batch_size) assert len(graph_model.layers) == 0
