Fix GraphConvTensorGraph to GraphConvModel in unittests (114caaac) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/test_graph_models.py

+6 −6

Original line number	Diff line number	Diff line
		@@ -4,7 +4,7 @@ import numpy as np

		import deepchem
		from deepchem.data import NumpyDataset
		from deepchem.models import GraphConvTensorGraph
		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
		@@ -43,7 +43,7 @@ class TestGraphModels(unittest.TestCase):
		'classification', 'GraphConv')

		batch_size = 50
		model = GraphConvTensorGraph(
		model = GraphConvModel(
		len(tasks), batch_size=batch_size, mode='classification')

		model.fit(dataset, nb_epoch=1)
		@@ -58,7 +58,7 @@ class TestGraphModels(unittest.TestCase):
		'regression', 'GraphConv')

		batch_size = 50
		model = GraphConvTensorGraph(
		model = GraphConvModel(
		len(tasks), batch_size=batch_size, mode='regression')

		model.fit(dataset, nb_epoch=1)
		@@ -73,7 +73,7 @@ class TestGraphModels(unittest.TestCase):
		'regression', 'GraphConv', num_tasks=1)

		batch_size = 50
		model = GraphConvTensorGraph(
		model = GraphConvModel(
		len(tasks), batch_size=batch_size, mode='regression')

		model.fit(dataset, nb_epoch=1)
		@@ -102,7 +102,7 @@ class TestGraphModels(unittest.TestCase):
		X = featurizer.featurize(dataset.X)
		dataset = deepchem.data.NumpyDataset(X, np.array(y))
		batch_size = 50
		model = GraphConvTensorGraph(
		model = GraphConvModel(
		len(tasks),
		number_atom_features=featurizer.feature_length(),
		batch_size=batch_size,
		@@ -121,7 +121,7 @@ class TestGraphModels(unittest.TestCase):
		'regression', 'GraphConv', num_tasks=1)

		batch_size = 50
		model = GraphConvTensorGraph(
		model = GraphConvModel(
		len(tasks), batch_size=batch_size, mode='regression')

		model.fit(dataset, nb_epoch=1)

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