WeaveTensorModel to WeaveModel (7994e125) · Commits · 钟慕尧 / deepchem

deepchem/models/init.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -17,7 +17,7 @@ from deepchem.models.tensorgraph.IRV import TensorflowMultiTaskIRVClassifier
		from deepchem.models.tensorgraph.robust_multitask import RobustMultitaskClassifier
		from deepchem.models.tensorgraph.robust_multitask import RobustMultitaskRegressor
		from deepchem.models.tensorgraph.progressive_multitask import ProgressiveMultitaskRegressor
		from deepchem.models.tensorgraph.models.graph_models import WeaveTensorModel, DTNNTensorGraph, DAGTensorGraph, GraphConvModel, MPNNTensorGraph
		from deepchem.models.tensorgraph.models.graph_models import WeaveModel, DTNNTensorGraph, DAGTensorGraph, GraphConvModel, MPNNTensorGraph
		from deepchem.models.tensorgraph.models.symmetry_function_regression import BPSymmetryFunctionRegression, ANIRegression

		from deepchem.models.tensorgraph.models.seqtoseq import SeqToSeq

+5 −5

Original line number	Diff line number	Diff line
		@@ -20,7 +20,7 @@ from deepchem.models.tensorgraph.tensor_graph import TensorGraph
		from deepchem.trans import undo_transforms


		class WeaveTensorModel(TensorGraph):
		class WeaveModel(TensorGraph):

		def __init__(self,
		n_tasks,
		@@ -52,7 +52,7 @@ class WeaveTensorModel(TensorGraph):
		self.n_hidden = n_hidden
		self.n_graph_feat = n_graph_feat
		self.mode = mode
		super(WeaveTensorModel, self).__init__(**kwargs)
		super(WeaveModel, self).__init__(**kwargs)
		self.build_graph()

		def build_graph(self):
		@@ -183,7 +183,7 @@ class WeaveTensorModel(TensorGraph):
		yield feed_dict

		def predict_on_generator(self, generator, transformers=[], outputs=None):
		out = super(WeaveTensorModel, self).predict_on_generator(
		out = super(WeaveModel, self).predict_on_generator(
		generator, transformers=[], outputs=outputs)
		if outputs is None:
		outputs = self.outputs
		@@ -1162,11 +1162,11 @@ class GraphConvTensorGraph(GraphConvModel):
		super(GraphConvTensorGraph, self).__init__(args, *kwargs)


		class WeaveTensorGraph(WeaveTensorModel):
		class WeaveTensorGraph(WeaveModel):

		warnings.warn(
		TENSORGRAPH_DEPRECATION.format("WeaveTensorGraph"), FutureWarning)

		def __init__(self, args, *kwargs):

		super(WeaveTensorModel, self).__init__(args, *kwargs)
		super(WeaveModel, self).__init__(args, *kwargs)

+2 −2

Original line number	Diff line number	Diff line
		@@ -8,7 +8,7 @@ from deepchem.models import GraphConvModel
		from deepchem.models import TensorGraph
		from deepchem.molnet.load_function.delaney_datasets import load_delaney
		from deepchem.models.tensorgraph.layers import ReduceSum, L2Loss
		from deepchem.models import WeaveTensorModel
		from deepchem.models import WeaveModel
		from deepchem.feat import ConvMolFeaturizer


		@@ -137,7 +137,7 @@ class TestGraphModels(unittest.TestCase):
		'regression', 'Weave', num_tasks=1)

		batch_size = 50
		model = WeaveTensorModel(
		model = WeaveModel(
		len(tasks), batch_size=batch_size, mode='regression', use_queue=False)

		model.fit(dataset, nb_epoch=1)

+2 −2

Original line number	Diff line number	Diff line
		@@ -698,7 +698,7 @@ class TestOverfit(test_util.TensorFlowTestCase):
		n_feat = 128
		batch_size = 10

		model = dc.models.WeaveTensorModel(
		model = dc.models.WeaveModel(
		n_tasks,
		n_atom_feat=n_atom_feat,
		n_pair_feat=n_pair_feat,
		@@ -738,7 +738,7 @@ class TestOverfit(test_util.TensorFlowTestCase):
		n_feat = 128
		batch_size = 10

		model = dc.models.WeaveTensorModel(
		model = dc.models.WeaveModel(
		n_tasks,
		n_atom_feat=n_atom_feat,
		n_pair_feat=n_pair_feat,

+2 −2

Original line number	Diff line number	Diff line
		@@ -242,7 +242,7 @@ def benchmark_classification(train_dataset,
		n_graph_feat = hyper_parameters['n_graph_feat']
		n_pair_feat = hyper_parameters['n_pair_feat']

		model = deepchem.models.WeaveTensorModel(
		model = deepchem.models.WeaveModel(
		len(tasks),
		n_atom_feat=n_features,
		n_pair_feat=n_pair_feat,
		@@ -577,7 +577,7 @@ def benchmark_regression(train_dataset,
		n_graph_feat = hyper_parameters['n_graph_feat']
		n_pair_feat = hyper_parameters['n_pair_feat']

		model = deepchem.models.WeaveTensorModel(
		model = deepchem.models.WeaveModel(
		len(tasks),
		n_atom_feat=n_features,
		n_pair_feat=n_pair_feat,