Commit 6f84277d authored by miaecle's avatar miaecle
Browse files

fix for failure

parent 8ae88ee5

deepchem/models/tf_new_models/graph_models.py

+6 −6
Original line number Diff line number Diff line
@@ -11,7 +11,7 @@ __license__ = "MIT" 


import tensorflow as tf

from deepchem.nn.layers import GraphGather

from deepchem.models.tf_new_models.graph_topology import GraphTopology, DTNNGraphTopology, DAGGraphTopology, WeaveGraphTopology, WeaveGraphTopology_v2

from deepchem.models.tf_new_models.graph_topology import GraphTopology, DTNNGraphTopology, DAGGraphTopology, WeaveGraphTopology, AlternateWeaveGraphTopology





class SequentialGraph(object):
@@ -196,8 +196,8 @@ class SequentialWeaveGraph(SequentialGraph): 
      self.layers.append(layer)





class SequentialWeaveGraph_v2(SequentialGraph):

  """SequentialGraph for Weave models

class AlternateSequentialWeaveGraph(SequentialGraph):

  """Alternate implementation of SequentialGraph for Weave models

  """



  def __init__(self, batch_size, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
@@ -207,7 +207,7 @@ class SequentialWeaveGraph_v2(SequentialGraph): 
    self.n_atom_feat = n_atom_feat

    self.n_pair_feat = n_pair_feat

    with self.graph.as_default():

      self.graph_topology = WeaveGraphTopology_v2(

      self.graph_topology = AlternateWeaveGraphTopology(

          self.batch_size, self.max_atoms, self.n_atom_feat, self.n_pair_feat)

      self.output = self.graph_topology.get_atom_features_placeholder()

      self.output_P = self.graph_topology.get_pair_features_placeholder()
@@ -216,11 +216,11 @@ class SequentialWeaveGraph_v2(SequentialGraph): 
  def add(self, layer):

    """Adds a new layer to model."""

    with self.graph.as_default():

      if type(layer).__name__ in ['WeaveLayer_v2']:

      if type(layer).__name__ in ['AlternateWeaveLayer']:

        self.output, self.output_P = layer([

            self.output, self.output_P

        ] + self.graph_topology.get_topology_placeholders())

      elif type(layer).__name__ in ['WeaveGather_v2']:

      elif type(layer).__name__ in ['AlternateWeaveGather']:

        self.output = layer(

            [self.output, self.graph_topology.atom_split_placeholder])

      else:

deepchem/models/tf_new_models/graph_topology.py

+1 −1
Original line number Diff line number Diff line
@@ -494,7 +494,7 @@ class WeaveGraphTopology(GraphTopology): 
    return dict_DTNN





class WeaveGraphTopology_v2(GraphTopology):

class AlternateWeaveGraphTopology(GraphTopology):

  """Manages placeholders associated with batch of graphs and their topology"""



  def __init__(self,

deepchem/molnet/run_benchmark_models.py

+12 −8
Original line number Diff line number Diff line
@@ -243,6 +243,7 @@ def benchmark_classification(train_dataset, 
      test_dataset.reshard(reshard_size)

      test_dataset = transformer.transform(test_dataset)



    tf.set_random_seed(seed)

    graph_model = deepchem.nn.SequentialDAGGraph(

        n_features, batch_size=batch_size, max_atoms=max_atoms)

    graph_model.add(
@@ -271,18 +272,19 @@ def benchmark_classification(train_dataset, 
    max_atoms_test = max([mol.get_num_atoms() for mol in test_dataset.X])

    max_atoms = max([max_atoms_train, max_atoms_valid, max_atoms_test])



    graph_model = deepchem.nn.SequentialWeaveGraph_v2(

    tf.set_random_seed(seed)

    graph_model = deepchem.nn.AlternateSequentialWeaveGraph(

        batch_size,

        max_atoms=max_atoms,

        n_atom_feat=n_features,

        n_pair_feat=n_pair_feat)

    graph_model.add(deepchem.nn.WeaveLayer_v2(max_atoms, 75, 14))

    graph_model.add(deepchem.nn.AlternateWeaveLayer(max_atoms, 75, 14))

    graph_model.add(

        deepchem.nn.WeaveLayer_v2(max_atoms, 50, 50, update_pair=False))

        deepchem.nn.AlternateWeaveLayer(max_atoms, 50, 50, update_pair=False))

    graph_model.add(deepchem.nn.Dense(n_graph_feat, 50, activation='tanh'))

    graph_model.add(deepchem.nn.BatchNormalization(epsilon=1e-5, mode=1))

    graph_model.add(

        deepchem.nn.WeaveGather_v2(

        deepchem.nn.AlternateWeaveGather(

            batch_size, n_input=n_graph_feat, gaussian_expand=True))



    model = deepchem.models.MultitaskGraphClassifier(
@@ -571,6 +573,7 @@ def benchmark_regression(train_dataset, 
      test_dataset.reshard(reshard_size)

      test_dataset = transformer.transform(test_dataset)



    tf.set_random_seed(seed)

    graph_model = deepchem.nn.SequentialDAGGraph(

        n_features, batch_size=batch_size, max_atoms=max_atoms)

    graph_model.add(
@@ -599,18 +602,19 @@ def benchmark_regression(train_dataset, 
    max_atoms_test = max([mol.get_num_atoms() for mol in test_dataset.X])

    max_atoms = max([max_atoms_train, max_atoms_valid, max_atoms_test])



    graph_model = deepchem.nn.SequentialWeaveGraph_v2(

    tf.set_random_seed(seed)

    graph_model = deepchem.nn.AlternateSequentialWeaveGraph(

        batch_size,

        max_atoms=max_atoms,

        n_atom_feat=n_features,

        n_pair_feat=n_pair_feat)

    graph_model.add(deepchem.nn.WeaveLayer_v2(max_atoms, 75, 14))

    graph_model.add(deepchem.nn.AlternateWeaveLayer(max_atoms, 75, 14))

    graph_model.add(

        deepchem.nn.WeaveLayer_v2(max_atoms, 50, 50, update_pair=False))

        deepchem.nn.AlternateWeaveLayer(max_atoms, 50, 50, update_pair=False))

    graph_model.add(deepchem.nn.Dense(n_graph_feat, 50, activation='tanh'))

    graph_model.add(deepchem.nn.BatchNormalization(epsilon=1e-5, mode=1))

    graph_model.add(

        deepchem.nn.WeaveGather_v2(

        deepchem.nn.AlternateWeaveGather(

            batch_size, n_input=n_graph_feat, gaussian_expand=True))



    model = deepchem.models.MultitaskGraphRegressor(

deepchem/molnet/tests/test_molnet.py

+8 −3
Original line number Diff line number Diff line
@@ -30,7 +30,7 @@ class TestMolnet(unittest.TestCase): 
    split = 'random'

    out_path = tempfile.mkdtemp()

    dc.molnet.run_benchmark(

        datasets, str(model), split=split, out_path=out_path)

        datasets, str(model), split=split, out_path=out_path, reload=False)

    with open(os.path.join(out_path, 'results.csv'), 'r') as f:

      reader = csv.reader(f)

      for lastrow in reader:
@@ -47,7 +47,7 @@ class TestMolnet(unittest.TestCase): 
    split = 'random'

    out_path = tempfile.mkdtemp()

    dc.molnet.run_benchmark(

        datasets, str(model), split=split, out_path=out_path)

        datasets, str(model), split=split, out_path=out_path, reload=False)

    with open(os.path.join(out_path, 'results.csv'), 'r') as f:

      reader = csv.reader(f)

      for lastrow in reader:
@@ -64,7 +64,12 @@ class TestMolnet(unittest.TestCase): 
    split = 'random'

    out_path = tempfile.mkdtemp()

    dc.molnet.run_benchmark(

        datasets, str(model), split=split, out_path=out_path, test=True)

        datasets,

        str(model),

        split=split,

        out_path=out_path,

        test=True,

        reload=False)

    with open(os.path.join(out_path, 'results.csv'), 'r') as f:

      reader = csv.reader(f)

      for lastrow in reader:

deepchem/nn/__init__.py

+4 −4
Original line number Diff line number Diff line
@@ -21,10 +21,10 @@ from deepchem.nn.layers import DAGLayer 
from deepchem.nn.layers import DAGGather



from deepchem.nn.weave_layers import WeaveLayer

from deepchem.nn.weave_layers import WeaveLayer_v2

from deepchem.nn.weave_layers import AlternateWeaveLayer

from deepchem.nn.weave_layers import WeaveConcat

from deepchem.nn.weave_layers import WeaveGather

from deepchem.nn.weave_layers import WeaveGather_v2

from deepchem.nn.weave_layers import AlternateWeaveGather



from deepchem.nn.model_ops import weight_decay

from deepchem.nn.model_ops import optimizer
@@ -38,10 +38,10 @@ from deepchem.models.tf_new_models.graph_topology import GraphTopology 
from deepchem.models.tf_new_models.graph_topology import DTNNGraphTopology

from deepchem.models.tf_new_models.graph_topology import DAGGraphTopology

from deepchem.models.tf_new_models.graph_topology import WeaveGraphTopology

from deepchem.models.tf_new_models.graph_topology import WeaveGraphTopology_v2

from deepchem.models.tf_new_models.graph_topology import AlternateWeaveGraphTopology

from deepchem.models.tf_new_models.graph_models import SequentialGraph

from deepchem.models.tf_new_models.graph_models import SequentialDTNNGraph

from deepchem.models.tf_new_models.graph_models import SequentialDAGGraph

from deepchem.models.tf_new_models.graph_models import SequentialWeaveGraph

from deepchem.models.tf_new_models.graph_models import SequentialWeaveGraph_v2

from deepchem.models.tf_new_models.graph_models import AlternateSequentialWeaveGraph

from deepchem.models.tf_new_models.graph_models import SequentialSupportGraph

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