Commit d3f8212a authored by Arun's avatar Arun
Browse files

fixed examples

parent d6368781

deepchem/models/torch_models/attentivefp.py

+9 −11
Original line number Original line Diff line number Diff line
@@ -189,17 +189,15 @@ class AttentiveFPModel(TorchModel): 




  Examples

  Examples

  --------

  --------



  >>> import deepchem as dc

  >>>

  >>> from deepchem.models import AttentiveFPModel

  >> import deepchem as dc

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

  >> from deepchem.models import AttentiveFPModel

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

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

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

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

  >>> train, valid, test = datasets

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

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

  >> train, valid, test = datasets

  ...    batch_size=32, learning_rate=0.001)

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

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

  ..                          batch_size=32, learning_rate=0.001)

  >> model.fit(train, nb_epoch=50)





  References

  References

  ----------

  ----------

deepchem/models/torch_models/gat.py

+9 −11
Original line number Original line Diff line number Diff line
@@ -227,17 +227,15 @@ class GATModel(TorchModel): 




  Examples

  Examples

  --------

  --------



  >>> import deepchem as dc

  >>>

  >>> from deepchem.models import GATModel

  >> import deepchem as dc

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

  >> from deepchem.models import GATModel

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

  >> featurizer = dc.feat.MolGraphConvFeaturizer()

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

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

  >>> train, valid, test = datasets

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

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

  >> train, valid, test = datasets

  ...                  batch_size=32, learning_rate=0.001)

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

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

  ..                  batch_size=32, learning_rate=0.001)

  >> model.fit(train, nb_epoch=50)





  References

  References

  ----------

  ----------

deepchem/models/torch_models/gcn.py

+9 −11
Original line number Original line Diff line number Diff line
@@ -211,17 +211,15 @@ class GCNModel(TorchModel): 




  Examples

  Examples

  --------

  --------



  >>> import deepchem as dc

  >>>

  >>> from deepchem.models import GCNModel

  >> import deepchem as dc

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

  >> from deepchem.models import GCNModel

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

  >> featurizer = dc.feat.MolGraphConvFeaturizer()

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

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

  >>> train, valid, test = datasets

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

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

  >> train, valid, test = datasets

  ...                  batch_size=32, learning_rate=0.001)

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

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

  ..                  batch_size=32, learning_rate=0.001)

  >> model.fit(train, nb_epoch=50)





  References

  References

  ----------

  ----------

deepchem/models/torch_models/mpnn.py

+9 −11
Original line number Original line Diff line number Diff line
@@ -188,17 +188,15 @@ class MPNNModel(TorchModel): 




  Examples

  Examples

  --------

  --------



  >>> import deepchem as dc

  >>>

  >>> from deepchem.models.torch_models import MPNNModel

  >> import deepchem as dc

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

  >> from deepchem.models.torch_models import MPNNModel

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

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

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

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

  >>> train, valid, test = datasets

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

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

  >> train, valid, test = datasets

  ...                  batch_size=32, learning_rate=0.001)

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

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

  ..                   batch_size=32, learning_rate=0.001)

  >> model.fit(train, nb_epoch=50)





  References

  References

  ----------

  ----------

deepchem/models/torch_models/pagtn.py

+9 −11
Original line number Original line Diff line number Diff line
@@ -197,17 +197,15 @@ class PagtnModel(TorchModel): 




  Examples

  Examples

  --------

  --------



  >>> import deepchem as dc

  >>>

  >>> from deepchem.models import PagtnModel

  >> import deepchem as dc

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

  >> from deepchem.models import PagtnModel

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

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

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

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

  >>> train, valid, test = datasets

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

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

  >> train, valid, test = datasets

  ...                    batch_size=16, learning_rate=0.001)

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

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

  ..                    batch_size=16, learning_rate=0.001)

  >> model.fit(train, nb_epoch=50)





  References

  References

  ----------

  ----------

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