Merge pull request #1167 from mlgill/issue1138_mg_GraphConvTensorGraph_to_GraphConvModel (7342aa8c) · Commits · 钟慕尧 / deepchem

deepchem/models/init.py

+5 −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 WeaveTensorGraph, DTNNTensorGraph, DAGTensorGraph, GraphConvTensorGraph, MPNNTensorGraph
		from deepchem.models.tensorgraph.models.graph_models import WeaveTensorGraph, DTNNTensorGraph, DAGTensorGraph, GraphConvModel, MPNNTensorGraph
		from deepchem.models.tensorgraph.models.symmetry_function_regression import BPSymmetryFunctionRegression, ANIRegression

		from deepchem.models.tensorgraph.models.seqtoseq import SeqToSeq
		@@ -25,3 +25,7 @@ from deepchem.models.tensorgraph.models.gan import GAN, WGAN
		from deepchem.models.tensorgraph.models.text_cnn import TextCNNTensorGraph
		from deepchem.models.tensorgraph.sequential import Sequential
		from deepchem.models.tensorgraph.models.sequence_dnn import SequenceDNN

		#################### Compatibility imports for renamed TensorGraph models. Remove below with DeepChem 3.0. ####################

		from deepchem.models.tensorgraph.models.graph_models import GraphConvTensorGraph
		No newline at end of file

deepchem/models/tensorgraph/models/graph_models.py

+19 −3

Original line number	Diff line number	Diff line
		@@ -664,7 +664,7 @@ class PetroskiSuchTensorGraph(TensorGraph):
		per_task_metrics=per_task_metrics)


		class GraphConvTensorGraph(TensorGraph):
		class GraphConvModel(TensorGraph):

		def __init__(self,
		n_tasks,
		@@ -700,7 +700,7 @@ class GraphConvTensorGraph(TensorGraph):
		self.graph_conv_layers = graph_conv_layers
		kwargs['use_queue'] = False
		self.number_atom_features = number_atom_features
		super(GraphConvTensorGraph, self).__init__(**kwargs)
		super(GraphConvModel, self).__init__(**kwargs)
		self.build_graph()

		def build_graph(self):
		@@ -824,7 +824,7 @@ class GraphConvTensorGraph(TensorGraph):
		result = undo_transforms(feed_results[0], transformers)
		feed_results = [result]
		for ind, result in enumerate(feed_results):
		# GraphConvTensorGraph constantly outputs batch_size number of
		# GraphConvModel constantly outputs batch_size number of
		# results, only valid samples should be appended to final results
		results[ind].append(result[:n_samples])

		@@ -1143,3 +1143,19 @@ class MPNNTensorGraph(TensorGraph):

		def predict_on_generator(self, generator, transformers=[]):
		return self.predict_proba_on_generator(generator, transformers)


		#################### Deprecation warnings for renamed TensorGraph models ####################

		import warnings


		class GraphConvTensorGraph(GraphConvModel):

		warnings.warn(
		"GraphConvTensorGraph is deprecated and has been renamed to GraphConvModel and will be removed in DeepChem 3.0.",
		FutureWarning)

		def __init__(self, args, *kwargs):

		super(GraphConvTensorGraph, self).__init__(args, *kwargs)

deepchem/models/tensorgraph/models/test_graph_models.py

+6 −9

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)
		@@ -58,8 +58,7 @@ class TestGraphModels(unittest.TestCase):
		'regression', 'GraphConv')

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

		model.fit(dataset, nb_epoch=1)
		scores = model.evaluate(dataset, [metric], transformers)
		@@ -73,8 +72,7 @@ class TestGraphModels(unittest.TestCase):
		'regression', 'GraphConv', num_tasks=1)

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

		model.fit(dataset, nb_epoch=1)

		@@ -102,7 +100,7 @@ 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,
		@@ -121,8 +119,7 @@ class TestGraphModels(unittest.TestCase):
		'regression', 'GraphConv', num_tasks=1)

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

		model.fit(dataset, nb_epoch=1)
		model.save()

deepchem/molnet/run_benchmark_models.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -191,7 +191,7 @@ def benchmark_classification(train_dataset,
		n_filters = hyper_parameters['n_filters']
		n_fully_connected_nodes = hyper_parameters['n_fully_connected_nodes']

		model = deepchem.models.GraphConvTensorGraph(
		model = deepchem.models.GraphConvModel(
		len(tasks),
		graph_conv_layers=[n_filters] * 2,
		dense_layer_size=n_fully_connected_nodes,
		@@ -507,7 +507,7 @@ def benchmark_regression(train_dataset,
		n_filters = hyper_parameters['n_filters']
		n_fully_connected_nodes = hyper_parameters['n_fully_connected_nodes']

		model = deepchem.models.GraphConvTensorGraph(
		model = deepchem.models.GraphConvModel(
		len(tasks),
		graph_conv_layers=[n_filters] * 2,
		dense_layer_size=n_fully_connected_nodes,

examples/adme/run_benchmarks.py

+2 −3

Original line number	Diff line number	Diff line
		@@ -14,7 +14,7 @@ from sklearn import svm
		import tensorflow as tf
		tf.set_random_seed(123)
		import deepchem as dc
		from deepchem.models.tensorgraph.models.graph_models import GraphConvTensorGraph
		from deepchem.models.tensorgraph.models.graph_models import GraphConvModel

		BATCH_SIZE = 128
		# Set to higher values to get better numbers
		@@ -74,8 +74,7 @@ def experiment(dataset_file, method='GraphConv', split='scaffold'):

		model = None
		if method == 'GraphConv':
		model = GraphConvTensorGraph(
		len(tasks), batch_size=BATCH_SIZE, mode="regression")
		model = GraphConvModel(len(tasks), batch_size=BATCH_SIZE, mode="regression")
		elif method == 'RF':

		def model_builder_rf(model_dir):

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