Commit 3b13162f authored by Arun's avatar Arun
Browse files

minor fixes

parent d3f8212a

deepchem/models/torch_models/attentivefp.py

+9 −6
Original line number Original line Diff line number Diff line
@@ -191,13 +191,16 @@ class AttentiveFPModel(TorchModel): 
  --------

  --------

  >>> import deepchem as dc

  >>> import deepchem as dc

  >>> from deepchem.models import AttentiveFPModel

  >>> from deepchem.models import AttentiveFPModel

  >>> # preparing dataset

  >>> smiles = ["C1CCC1", "C1=CC=CN=C1"]

  >>> labels = [0., 1.]

  >>> featurizer = dc.feat.MolGraphConvFeaturizer(use_edges=True)

  >>> featurizer = dc.feat.MolGraphConvFeaturizer(use_edges=True)

  >>> tasks, datasets, transformers = dc.molnet.load_tox21(

  >>> X = featurizer.featurize(smiles)

  ...    reload=False, featurizer=featurizer, transformers=[])

  >>> dataset = dc.data.NumpyDataset(X=X, y=labels)

  >>> train, valid, test = datasets

  >>> # training model

  >>> model = AttentiveFPModel(mode='classification', n_tasks=len(tasks),

  >>> model = AttentiveFPModel(mode='classification', n_tasks=1,

  ...    batch_size=32, learning_rate=0.001)

  ...    batch_size=16, learning_rate=0.001)

  >>> loss = model.fit(train, nb_epoch=10)

  >>> loss = model.fit(dataset, nb_epoch=5)





  References

  References

  ----------

  ----------

deepchem/models/torch_models/gat.py

+9 −6
Original line number Original line Diff line number Diff line
@@ -229,13 +229,16 @@ class GATModel(TorchModel): 
  --------

  --------

  >>> import deepchem as dc

  >>> import deepchem as dc

  >>> from deepchem.models import GATModel

  >>> from deepchem.models import GATModel

  >>> # preparing dataset

  >>> smiles = ["C1CCC1", "C1=CC=CN=C1"]

  >>> labels = [0., 1.]

  >>> featurizer = dc.feat.MolGraphConvFeaturizer()

  >>> featurizer = dc.feat.MolGraphConvFeaturizer()

  >>> tasks, datasets, transformers = dc.molnet.load_tox21(

  >>> X = featurizer.featurize(smiles)

  ...    reload=False, featurizer=featurizer, transformers=[])

  >>> dataset = dc.data.NumpyDataset(X=X, y=labels)

  >>> train, valid, test = datasets

  >>> # training model

  >>> model = GATModel(mode='classification', n_tasks=len(tasks),

  >>> model = GATModel(mode='classification', n_tasks=1,

  ...                  batch_size=32, learning_rate=0.001)

  ...                  batch_size=16, learning_rate=0.001)

  >>> loss = model.fit(train, nb_epoch=10)

  >>> loss = model.fit(dataset, nb_epoch=5)





  References

  References

  ----------

  ----------

deepchem/models/torch_models/gcn.py

+9 −6
Original line number Original line Diff line number Diff line
@@ -213,13 +213,16 @@ class GCNModel(TorchModel): 
  --------

  --------

  >>> import deepchem as dc

  >>> import deepchem as dc

  >>> from deepchem.models import GCNModel

  >>> from deepchem.models import GCNModel

  >>> # preparing dataset

  >>> smiles = ["C1CCC1", "CCC"]

  >>> labels = [0., 1.]

  >>> featurizer = dc.feat.MolGraphConvFeaturizer()

  >>> featurizer = dc.feat.MolGraphConvFeaturizer()

  >>> tasks, datasets, transformers = dc.molnet.load_tox21(

  >>> X = featurizer.featurize(smiles)

  ...     reload=False, featurizer=featurizer, transformers=[])

  >>> dataset = dc.data.NumpyDataset(X=X, y=labels)

  >>> train, valid, test = datasets

  >>> # training model

  >>> model = GCNModel(mode='classification', n_tasks=len(tasks),

  >>> model = GCNModel(mode='classification', n_tasks=1,

  ...                  batch_size=32, learning_rate=0.001)

  ...                  batch_size=16, learning_rate=0.001)

  >>> loss = model.fit(train, nb_epoch=10)

  >>> loss = model.fit(dataset, nb_epoch=5)





  References

  References

  ----------

  ----------

deepchem/models/torch_models/mpnn.py

+9 −6
Original line number Original line Diff line number Diff line
@@ -190,13 +190,16 @@ class MPNNModel(TorchModel): 
  --------

  --------

  >>> import deepchem as dc

  >>> import deepchem as dc

  >>> from deepchem.models.torch_models import MPNNModel

  >>> from deepchem.models.torch_models import MPNNModel

  >>> # preparing dataset

  >>> smiles = ["C1CCC1", "CCC"]

  >>> labels = [0., 1.]

  >>> featurizer = dc.feat.MolGraphConvFeaturizer(use_edges=True)

  >>> featurizer = dc.feat.MolGraphConvFeaturizer(use_edges=True)

  >>> tasks, datasets, transformers = dc.molnet.load_tox21(

  >>> X = featurizer.featurize(smiles)

  ...     reload=False, featurizer=featurizer, transformers=[])

  >>> dataset = dc.data.NumpyDataset(X=X, y=labels)

  >>> train, valid, test = datasets

  >>> # training model

  >>> model = MPNNModel(mode='classification', n_tasks=len(tasks),

  >>> model = MPNNModel(mode='classification', n_tasks=1,

  ...                  batch_size=32, learning_rate=0.001)

  ...                  batch_size=16, learning_rate=0.001)

  >>> loss =  model.fit(train, nb_epoch=10)

  >>> loss =  model.fit(dataset, nb_epoch=5)





  References

  References

  ----------

  ----------

deepchem/models/torch_models/pagtn.py

+8 −5
Original line number Original line Diff line number Diff line
@@ -199,13 +199,16 @@ class PagtnModel(TorchModel): 
  --------

  --------

  >>> import deepchem as dc

  >>> import deepchem as dc

  >>> from deepchem.models import PagtnModel

  >>> from deepchem.models import PagtnModel

  >>> # preparing dataset

  >>> smiles = ["C1CCC1", "CCC"]

  >>> labels = [0., 1.]

  >>> featurizer = dc.feat.PagtnMolGraphFeaturizer(max_length=5)

  >>> featurizer = dc.feat.PagtnMolGraphFeaturizer(max_length=5)

  >>> tasks, datasets, transformers = dc.molnet.load_tox21(

  >>> X = featurizer.featurize(smiles)

  ...     reload=False, featurizer=featurizer, transformers=[])

  >>> dataset = dc.data.NumpyDataset(X=X, y=labels)

  >>> train, valid, test = datasets

  >>> # training model

  >>> model = PagtnModel(mode='classification', n_tasks=len(tasks),

  >>> model = PagtnModel(mode='classification', n_tasks=1,

  ...                    batch_size=16, learning_rate=0.001)

  ...                    batch_size=16, learning_rate=0.001)

  >>> loss = model.fit(train, nb_epoch=10)

  >>> loss = model.fit(dataset, nb_epoch=5)





  References

  References

  ----------

  ----------

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