Merge pull request #528 from miaecle/DTNN (4050c527) · Commits · 钟慕尧 / deepchem

README.md

+15 −8

Original line number	Diff line number	Diff line
		@@ -496,26 +496,29 @@ Scaffold splitting
		\| \|Graphconv regression\|Scaffold \|0.695 \|0.391 \|
		\| \|Weave regression \|Scaffold \|0.401 \|0.373 \|
		\|qm7 \|NN regression \|Index \|0.997 \|0.992 \|
		\| \|DTNN \|Index \|0.998 \|0.996 \|
		\| \|DTNN \|Index \|0.997 \|0.995 \|
		\| \|NN regression \|Random \|0.998 \|0.997 \|
		\| \|DTNN \|Random \|0.998 \|0.998 \|
		\| \|DTNN \|Random \|0.999 \|0.998 \|
		\| \|NN regression \|Stratified \|0.998 \|0.997 \|
		\| \|DTNN \|Stratified \|0.998 \|0.998 \|
		\|qm7b \|MT-NN regression \|Index \|0.903 \|0.789 \|
		\| \|DTNN \|Index \|0.872 \|0.821 \|
		\| \|DTNN \|Index \|0.919 \|0.863 \|
		\| \|MT-NN regression \|Random \|0.893 \|0.839 \|
		\| \|DTNN \|Random \|0.865 \|0.849 \|
		\| \|DTNN \|Random \|0.924 \|0.898 \|
		\| \|MT-NN regression \|Stratified \|0.891 \|0.859 \|
		\| \|DTNN \|Stratified \|0.853 \|0.839 \|
		\| \|DTNN \|Stratified \|0.913 \|0.894 \|
		\|qm8 \|MT-NN regression \|Index \|0.783 \|0.656 \|
		\| \|DTNN \|Index \|0.737 \|0.639 \|
		\| \|DTNN \|Index \|0.857 \|0.691 \|
		\| \|MT-NN regression \|Random \|0.747 \|0.660 \|
		\| \|DTNN \|Random \|0.731 \|0.711 \|
		\| \|DTNN \|Random \|0.842 \|0.756 \|
		\| \|MT-NN regression \|Stratified \|0.756 \|0.681 \|
		\| \|DTNN \|Stratified \|0.714 \|0.683 \|
		\| \|DTNN \|Stratified \|0.844 \|0.758 \|
		\|qm9 \|MT-NN regression \|Index \|0.733 \|0.766 \|
		\| \|DTNN \|Index \|0.918 \|0.831 \|
		\| \|MT-NN regression \|Random \|0.852 \|0.833 \|
		\| \|DTNN \|Random \|0.942 \|0.948 \|
		\| \|MT-NN regression \|Stratified \|0.764 \|0.792 \|
		\| \|DTNN \|Stratified \|0.941 \|0.867 \|
		\|sampl \|Random forest \|Index \|0.968 \|0.736 \|
		\| \|XGBoost \|Index \|0.884 \|0.784 \|
		\| \|NN regression \|Index \|0.917 \|0.764 \|
		@@ -675,9 +678,13 @@ Time needed for benchmark test(~20h in total)
		\| \|Graphconv regression\|20 \|100 \|
		\| \|Weave regression \|20 \|120 \|
		\|qm7 \|MT-NN regression \|10 \|400 \|
		\| \|DTNN \|10 \|600 \|
		\|qm7b \|MT-NN regression \|10 \|600 \|
		\| \|DTNN \|10 \|600 \|
		\|qm8 \|MT-NN regression \|60 \|1000 \|
		\| \|DTNN \|10 \|2000 \|
		\|qm9 \|MT-NN regression \|220 \|10000 \|
		\| \|DTNN \|10 \|14000 \|
		\|sampl \|NN regression \|10 \|30 \|
		\| \|XGBoost \|10 \|20 \|
		\| \|Random forest \|10 \|20 \|

deepchem/models/init.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -10,7 +10,6 @@ from deepchem.models.sklearn_models import SklearnModel
		from deepchem.models.xgboost_models import XGBoostModel
		from deepchem.models.tf_new_models.multitask_classifier import MultitaskGraphClassifier
		from deepchem.models.tf_new_models.multitask_regressor import MultitaskGraphRegressor
		from deepchem.models.tf_new_models.DTNN_regressor import DTNNGraphRegressor

		from deepchem.models.tf_new_models.support_classifier import SupportGraphClassifier
		from deepchem.models.multitask import SingletaskToMultitask
		@@ -26,3 +25,4 @@ from deepchem.models.tensorflow_models.progressive_multitask import ProgressiveM
		from deepchem.models.tensorflow_models.progressive_joint import ProgressiveJointRegressor
		from deepchem.models.tensorflow_models.IRV import TensorflowMultiTaskIRVClassifier
		from deepchem.models.tensorgraph.tensor_graph import TensorGraph, MultiTaskTensorGraph
		from deepchem.models.tensorgraph.models.graph_models import WeaveTensorGraph, DTNNTensorGraph, DAGTensorGraph
		No newline at end of file

Original line number	Diff line number	Diff line
		@@ -496,26 +496,29 @@ Scaffold splitting
		\| \|Graphconv regression\|Scaffold \|0.695 \|0.391 \|
		\| \|Weave regression \|Scaffold \|0.401 \|0.373 \|
		\|qm7 \|NN regression \|Index \|0.997 \|0.992 \|
		\| \|DTNN \|Index \|0.998 \|0.996 \|
		\| \|DTNN \|Index \|0.997 \|0.995 \|
		\| \|NN regression \|Random \|0.998 \|0.997 \|
		\| \|DTNN \|Random \|0.998 \|0.998 \|
		\| \|DTNN \|Random \|0.999 \|0.998 \|
		\| \|NN regression \|Stratified \|0.998 \|0.997 \|
		\| \|DTNN \|Stratified \|0.998 \|0.998 \|
		\|qm7b \|MT-NN regression \|Index \|0.903 \|0.789 \|
		\| \|DTNN \|Index \|0.872 \|0.821 \|
		\| \|DTNN \|Index \|0.919 \|0.863 \|
		\| \|MT-NN regression \|Random \|0.893 \|0.839 \|
		\| \|DTNN \|Random \|0.865 \|0.849 \|
		\| \|DTNN \|Random \|0.924 \|0.898 \|
		\| \|MT-NN regression \|Stratified \|0.891 \|0.859 \|
		\| \|DTNN \|Stratified \|0.853 \|0.839 \|
		\| \|DTNN \|Stratified \|0.913 \|0.894 \|
		\|qm8 \|MT-NN regression \|Index \|0.783 \|0.656 \|
		\| \|DTNN \|Index \|0.737 \|0.639 \|
		\| \|DTNN \|Index \|0.857 \|0.691 \|
		\| \|MT-NN regression \|Random \|0.747 \|0.660 \|
		\| \|DTNN \|Random \|0.731 \|0.711 \|
		\| \|DTNN \|Random \|0.842 \|0.756 \|
		\| \|MT-NN regression \|Stratified \|0.756 \|0.681 \|
		\| \|DTNN \|Stratified \|0.714 \|0.683 \|
		\| \|DTNN \|Stratified \|0.844 \|0.758 \|
		\|qm9 \|MT-NN regression \|Index \|0.733 \|0.766 \|
		\| \|DTNN \|Index \|0.918 \|0.831 \|
		\| \|MT-NN regression \|Random \|0.852 \|0.833 \|
		\| \|DTNN \|Random \|0.942 \|0.948 \|
		\| \|MT-NN regression \|Stratified \|0.764 \|0.792 \|
		\| \|DTNN \|Stratified \|0.941 \|0.867 \|
		\|sampl \|Random forest \|Index \|0.968 \|0.736 \|
		\| \|XGBoost \|Index \|0.884 \|0.784 \|
		\| \|NN regression \|Index \|0.917 \|0.764 \|
		@@ -675,9 +678,13 @@ Time needed for benchmark test(~20h in total)
		\| \|Graphconv regression\|20 \|100 \|
		\| \|Weave regression \|20 \|120 \|
		\|qm7 \|MT-NN regression \|10 \|400 \|
		\| \|DTNN \|10 \|600 \|
		\|qm7b \|MT-NN regression \|10 \|600 \|
		\| \|DTNN \|10 \|600 \|
		\|qm8 \|MT-NN regression \|60 \|1000 \|
		\| \|DTNN \|10 \|2000 \|
		\|qm9 \|MT-NN regression \|220 \|10000 \|
		\| \|DTNN \|10 \|14000 \|
		\|sampl \|NN regression \|10 \|30 \|
		\| \|XGBoost \|10 \|20 \|
		\| \|Random forest \|10 \|20 \|

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