Commit 2c22772e authored by peastman's avatar peastman
Browse files

Simplified names of TensorGraphMultiTask models

parent 9af0485d

deepchem/models/__init__.py

+3 −3
Original line number Diff line number Diff line
@@ -18,9 +18,9 @@ from deepchem.models.sequential import Sequential 
from deepchem.models.tensorflow_models.fcnet import TensorflowMultiTaskRegressor

from deepchem.models.tensorflow_models.fcnet import TensorflowMultiTaskClassifier

from deepchem.models.tensorflow_models.fcnet import TensorflowMultiTaskFitTransformRegressor

from deepchem.models.tensorflow_models.fcnet import TensorGraphMultiTaskRegressor

from deepchem.models.tensorflow_models.fcnet import TensorGraphMultiTaskClassifier

from deepchem.models.tensorflow_models.fcnet import TensorGraphMultiTaskFitTransformRegressor

from deepchem.models.tensorflow_models.fcnet import MultiTaskRegressor

from deepchem.models.tensorflow_models.fcnet import MultiTaskClassifier

from deepchem.models.tensorflow_models.fcnet import MultiTaskFitTransformRegressor

from deepchem.models.tensorflow_models.robust_multitask import RobustMultitaskRegressor

from deepchem.models.tensorflow_models.robust_multitask import RobustMultitaskClassifier

from deepchem.models.tensorflow_models.lr import TensorflowLogisticRegression

deepchem/models/tensorflow_models/fcnet.py

+17 −18
Original line number Diff line number Diff line
@@ -25,7 +25,7 @@ from deepchem.models.tensorgraph.tensor_graph import TensorGraph, TFWrapper 
from deepchem.models.tensorgraph.layers import Feature, Label, Weights, WeightedError, Dense, Dropout, WeightDecay, Reshape, SoftMaxCrossEntropy, L2Loss, ReduceSum





class TensorGraphMultiTaskClassifier(TensorGraph):

class MultiTaskClassifier(TensorGraph):



  def __init__(self,

               n_tasks,
@@ -39,7 +39,7 @@ class TensorGraphMultiTaskClassifier(TensorGraph): 
               activation_fns=tf.nn.relu,

               n_classes=2,

               **kwargs):

    """Create a TensorGraphMultiTaskClassifier.

    """Create a MultiTaskClassifier.



    In addition to the following arguments, this class also accepts all the keywork arguments

    from TensorGraph.
@@ -73,7 +73,7 @@ class TensorGraphMultiTaskClassifier(TensorGraph): 
    n_classes: int

      the number of classes

    """

    super(TensorGraphMultiTaskClassifier, self).__init__(**kwargs)

    super(MultiTaskClassifier, self).__init__(**kwargs)

    self.n_tasks = n_tasks

    self.n_features = n_features

    self.n_classes = n_classes
@@ -152,8 +152,8 @@ class TensorGraphMultiTaskClassifier(TensorGraph): 
        yield feed_dict



  def predict_proba(self, dataset, transformers=[], outputs=None):

    return super(TensorGraphMultiTaskClassifier, self).predict(

        dataset, transformers, outputs)

    return super(MultiTaskClassifier, self).predict(dataset, transformers,

                                                    outputs)



  def predict(self, dataset, transformers=[], outputs=None):

    """
@@ -176,13 +176,13 @@ class TensorGraphMultiTaskClassifier(TensorGraph): 
    y_pred: numpy ndarray or list of numpy ndarrays

    """

    # Results is of shape (n_samples, n_tasks, n_classes)

    retval = super(TensorGraphMultiTaskClassifier, self).predict(

        dataset, transformers, outputs)

    retval = super(MultiTaskClassifier, self).predict(dataset, transformers,

                                                      outputs)

    # retval is of shape (n_samples, n_tasks)

    return np.argmax(retval, axis=2)





class TensorGraphMultiTaskRegressor(TensorGraph):

class MultiTaskRegressor(TensorGraph):



  def __init__(self,

               n_tasks,
@@ -195,7 +195,7 @@ class TensorGraphMultiTaskRegressor(TensorGraph): 
               dropouts=0.5,

               activation_fns=tf.nn.relu,

               **kwargs):

    """Create a TensorGraphMultiTaskRegressor.

    """Create a MultiTaskRegressor.



    In addition to the following arguments, this class also accepts all the keywork arguments

    from TensorGraph.
@@ -228,7 +228,7 @@ class TensorGraphMultiTaskRegressor(TensorGraph): 
      len(layer_sizes).  Alternatively this may be a single value instead of a list, in which case the

      same value is used for every layer.

    """

    super(TensorGraphMultiTaskRegressor, self).__init__(**kwargs)

    super(MultiTaskRegressor, self).__init__(**kwargs)

    self.n_tasks = n_tasks

    self.n_features = n_features

    n_layers = len(layer_sizes)
@@ -309,8 +309,8 @@ class TensorGraphMultiTaskRegressor(TensorGraph): 
        yield feed_dict





class TensorGraphMultiTaskFitTransformRegressor(TensorGraphMultiTaskRegressor):

  """Implements a TensorGraphMultiTaskRegressor that performs on-the-fly transformation during fit/predict.

class MultiTaskFitTransformRegressor(MultiTaskRegressor):

  """Implements a MultiTaskRegressor that performs on-the-fly transformation during fit/predict.



  Example:
@@ -336,10 +336,10 @@ class TensorGraphMultiTaskFitTransformRegressor(TensorGraphMultiTaskRegressor): 
               n_evals=1,

               batch_size=50,

               **kwargs):

    """Create a TensorGraphMultiTaskFitTransformRegressor.

    """Create a MultiTaskFitTransformRegressor.



    In addition to the following arguments, this class also accepts all the keywork arguments

    from TensorGraphMultiTaskRegressor.

    from MultiTaskRegressor.



    Parameters

    ----------
@@ -367,7 +367,7 @@ class TensorGraphMultiTaskFitTransformRegressor(TensorGraphMultiTaskRegressor): 
      X_b = transformer.X_transform(X_b)

    n_features = X_b.shape[1]

    print("n_features after fit_transform: %d" % int(n_features))

    super(TensorGraphMultiTaskFitTransformRegressor, self).__init__(

    super(MultiTaskFitTransformRegressor, self).__init__(

        n_tasks, n_features, batch_size=batch_size, **kwargs)



  def default_generator(self,
@@ -405,9 +405,8 @@ class TensorGraphMultiTaskFitTransformRegressor(TensorGraphMultiTaskRegressor): 
        feed_dict[self.features[0]] = X_t

        yield feed_dict



    return super(TensorGraphMultiTaskFitTransformRegressor,

                 self).predict_on_generator(transform_generator(), transformers,

                                            outputs)

    return super(MultiTaskFitTransformRegressor, self).predict_on_generator(

        transform_generator(), transformers, outputs)





class TensorflowMultiTaskClassifier(TensorflowClassifier):

deepchem/models/tensorgraph/models/robust_multitask.py

+1 −1
Original line number Diff line number Diff line
@@ -11,7 +11,7 @@ 
#    return self.out_tensor

#

#

#def tensorGraphMultitaskClassifier(n_tasks,

#def MultiTaskClassifier(n_tasks,

#                                   n_features,

#                                   layer_sizes=[500],

#                                   bypass_layer_sizes=[100],

deepchem/models/tests/test_api.py

+1 −1
Original line number Diff line number Diff line
@@ -215,7 +215,7 @@ class TestAPI(unittest.TestCase): 
        dc.metrics.Metric(dc.metrics.accuracy_score)

    ]



    model = dc.models.TensorGraphMultiTaskClassifier(len(tasks), n_features)

    model = dc.models.MultiTaskClassifier(len(tasks), n_features)



    # Test Parameter getting and setting

    param, value = 'weight_decay_penalty_type', 'l2'

deepchem/models/tests/test_overfit.py

+7 −7
Original line number Diff line number Diff line
@@ -164,7 +164,7 @@ class TestOverfit(test_util.TensorFlowTestCase): 


    regression_metric = dc.metrics.Metric(dc.metrics.mean_squared_error)

    # TODO(rbharath): This breaks with optimizer="momentum". Why?

    model = dc.models.TensorGraphMultiTaskRegressor(

    model = dc.models.MultiTaskRegressor(

        n_tasks,

        n_features,

        dropouts=[0.],
@@ -228,7 +228,7 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    dataset = dc.data.NumpyDataset(X, y, w, ids)



    classification_metric = dc.metrics.Metric(dc.metrics.accuracy_score)

    model = dc.models.TensorGraphMultiTaskClassifier(

    model = dc.models.MultiTaskClassifier(

        n_tasks,

        n_features,

        dropouts=[0.],
@@ -293,7 +293,7 @@ class TestOverfit(test_util.TensorFlowTestCase): 


    fit_transformers = [dc.trans.CoulombFitTransformer(dataset)]

    regression_metric = dc.metrics.Metric(dc.metrics.mean_squared_error)

    model = dc.models.TensorGraphMultiTaskFitTransformRegressor(

    model = dc.models.MultiTaskFitTransformRegressor(

        n_tasks, [n_features, n_features],

        dropouts=[0.],

        weight_init_stddevs=[np.sqrt(6) / np.sqrt(1000)],
@@ -364,7 +364,7 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    dataset = dc.data.NumpyDataset(X, y, w, ids)



    classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score)

    model = dc.models.TensorGraphMultiTaskClassifier(

    model = dc.models.MultiTaskClassifier(

        n_tasks,

        n_features,

        dropouts=[0.],
@@ -454,7 +454,7 @@ class TestOverfit(test_util.TensorFlowTestCase): 
    dataset = dc.data.DiskDataset.from_numpy(X, y, w, ids)



    classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score)

    model = dc.models.TensorGraphMultiTaskClassifier(

    model = dc.models.MultiTaskClassifier(

        n_tasks,

        n_features,

        dropouts=[0.],
@@ -553,7 +553,7 @@ class TestOverfit(test_util.TensorFlowTestCase): 


    classification_metric = dc.metrics.Metric(

        dc.metrics.accuracy_score, task_averager=np.mean)

    model = dc.models.TensorGraphMultiTaskClassifier(

    model = dc.models.MultiTaskClassifier(

        n_tasks,

        n_features,

        dropouts=[0.],
@@ -751,7 +751,7 @@ class TestOverfit(test_util.TensorFlowTestCase): 


    regression_metric = dc.metrics.Metric(

        dc.metrics.mean_squared_error, task_averager=np.mean, mode="regression")

    model = dc.models.TensorGraphMultiTaskRegressor(

    model = dc.models.MultiTaskRegressor(

        n_tasks,

        n_features,

        dropouts=[0.],

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