Loading devtools/jenkins/compare_results.py +1 −0 Original line number Diff line number Diff line Loading @@ -7,6 +7,7 @@ BENCHMARK_TO_DESIRED_KEY_MAP = { "index": "Index splitting", "random": "Random splitting", "scaffold": "Scaffold splitting", "stratified": "Stratified splitting", "logreg": "Logistic regression", "rf": "Random forest", "tf": "NN classification", Loading devtools/jenkins/desired_results.csv +380 −374 Original line number Diff line number Diff line Loading @@ -257,16 +257,19 @@ Index splitting,lipo,NN regression,0.829,0.508 Index splitting,lipo,Graphconv regression,0.867,0.702 Index splitting,lipo,DAG regression,0.957,0.483 Index splitting,lipo,Weave regression,0.726,0.607 Index splitting,lipo,MPNN,0.963,0.68 Random splitting,lipo,Random forest,0.96,0.514 Random splitting,lipo,NN regression,0.833,0.476 Random splitting,lipo,Graphconv regression,0.867,0.631 Random splitting,lipo,DAG regression,0.967,0.412 Random splitting,lipo,Weave regression,0.747,0.598 Random splitting,lipo,MPNN,0.964,0.66 Scaffold splitting,lipo,Random forest,0.959,0.33 Scaffold splitting,lipo,NN regression,0.83,0.308 Scaffold splitting,lipo,Graphconv regression,0.875,0.608 Scaffold splitting,lipo,DAG regression,0.937,0.368 Scaffold splitting,lipo,Weave regression,0.761,0.575 Scaffold splitting,lipo,MPNN,0.954,0.537 Index splitting,nci,XGBoost regression,0.441,0.066 Index splitting,nci,NN regression,0.69,0.062 Index splitting,nci,Graphconv regression,0.123,0.053 Loading Loading @@ -329,16 +332,19 @@ Index splitting,qm8,NN regression,0.939,0.604 Index splitting,qm8,Graphconv regression,0.866,0.704 Index splitting,qm8,NN regression(CM),0.77,0.625 Index splitting,qm8,DTNN,0.856,0.696 Index splitting,qm8,MPNN,0.836,0.704 Random splitting,qm8,Random forest,0.971,0.706 Random splitting,qm8,NN regression,0.934,0.717 Random splitting,qm8,Graphconv regression,0.848,0.78 Random splitting,qm8,NN regression(CM),0.753,0.699 Random splitting,qm8,DTNN,0.842,0.754 Random splitting,qm8,MPNN,0.828,0.764 Stratified splitting,qm8,Random forest,0.971,0.69 Stratified splitting,qm8,NN regression,0.934,0.712 Stratified splitting,qm8,Graphconv regression,0.846,0.767 Stratified splitting,qm8,NN regression(CM),0.761,0.696 Stratified splitting,qm8,DTNN,0.846,0.745 Stratified splitting,qm8,MPNN,0.839,0.752 Index splitting,qm9,NN regression,0.839,0.708 Index splitting,qm9,Graphconv regression,0.754,0.768 Index splitting,qm9,NN regression(CM),0.803,0.8 Loading devtools/jenkins/jenkins.sh +15 −6 Original line number Diff line number Diff line Loading @@ -10,13 +10,22 @@ export DEEPCHEM_DATA_DIR='/tmp/molnet_'$envname mkdir $DEEPCHEM_DATA_DIR cd examples python benchmark.py -d tox21 -m weave -m graphconv -m tf_robust -m tf -m irv -m xgb -m logreg --seed 123 export retval_tox21=$? python benchmark.py -d hiv -m graphconv -m tf -m irv -m logreg --seed 123 export retval_hiv=$? python benchmark.py -d clintox -m weave -m graphconv -m tf -m tf_robust -m irv -m xgb -m logreg --seed 123 python benchmark.py -d sider -m weave -m graphconv -m tf -m tf_robust -m irv -m logreg --seed 123 python benchmark.py -d bbbp -m weave -m graphconv -m tf -m irv -m logreg --seed 123 python benchmark.py -d hiv -m weave -m graphconv -m tf -m irv -m logreg --seed 123 python benchmark.py -d muv -m weave -m graphconv -m tf -m tf_robust -m irv -m logreg --seed 123 python benchmark.py -d bace -m weave -m graphconv -m tf -m irv -m logreg --seed 123 python benchmark.py -d delaney -m weave_regression -m graphconvreg -m tf_regression -m dag_regression -m mpnn --seed 123 export retval_delaney=$? python benchmark.py -d qm7 -m dtnn -m graphconvreg -m tf_regression_ft --seed 123 export retval_qm7=$? python benchmark.py -d sampl -m weave_regression -m graphconvreg -m tf_regression -m dag_regression -m mpnn --seed 123 python benchmark.py -d lipo -m weave_regression -m graphconvreg -m tf_regression -m dag_regression -m mpnn --seed 123 python benchmark.py -d qm7 -m dtnn -m graphconvreg -m tf_regression_ft -m tf_regression -s random -s stratified --seed 123 python benchmark.py -d qm7b -m dtnn -m tf_regression_ft --seed 123 python benchmark.py -d qm8 -m dtnn -m mpnn -m graphconvreg -m tf_regression -m tf_regression_ft --seed 123 python benchmark.py -d qm9 -m dtnn -m graphconvreg -m tf_regression -m tf_regression_ft --seed 123 cd .. nosetests -v devtools/jenkins/compare_results.py --with-xunit || true Loading Loading
devtools/jenkins/compare_results.py +1 −0 Original line number Diff line number Diff line Loading @@ -7,6 +7,7 @@ BENCHMARK_TO_DESIRED_KEY_MAP = { "index": "Index splitting", "random": "Random splitting", "scaffold": "Scaffold splitting", "stratified": "Stratified splitting", "logreg": "Logistic regression", "rf": "Random forest", "tf": "NN classification", Loading
devtools/jenkins/desired_results.csv +380 −374 Original line number Diff line number Diff line Loading @@ -257,16 +257,19 @@ Index splitting,lipo,NN regression,0.829,0.508 Index splitting,lipo,Graphconv regression,0.867,0.702 Index splitting,lipo,DAG regression,0.957,0.483 Index splitting,lipo,Weave regression,0.726,0.607 Index splitting,lipo,MPNN,0.963,0.68 Random splitting,lipo,Random forest,0.96,0.514 Random splitting,lipo,NN regression,0.833,0.476 Random splitting,lipo,Graphconv regression,0.867,0.631 Random splitting,lipo,DAG regression,0.967,0.412 Random splitting,lipo,Weave regression,0.747,0.598 Random splitting,lipo,MPNN,0.964,0.66 Scaffold splitting,lipo,Random forest,0.959,0.33 Scaffold splitting,lipo,NN regression,0.83,0.308 Scaffold splitting,lipo,Graphconv regression,0.875,0.608 Scaffold splitting,lipo,DAG regression,0.937,0.368 Scaffold splitting,lipo,Weave regression,0.761,0.575 Scaffold splitting,lipo,MPNN,0.954,0.537 Index splitting,nci,XGBoost regression,0.441,0.066 Index splitting,nci,NN regression,0.69,0.062 Index splitting,nci,Graphconv regression,0.123,0.053 Loading Loading @@ -329,16 +332,19 @@ Index splitting,qm8,NN regression,0.939,0.604 Index splitting,qm8,Graphconv regression,0.866,0.704 Index splitting,qm8,NN regression(CM),0.77,0.625 Index splitting,qm8,DTNN,0.856,0.696 Index splitting,qm8,MPNN,0.836,0.704 Random splitting,qm8,Random forest,0.971,0.706 Random splitting,qm8,NN regression,0.934,0.717 Random splitting,qm8,Graphconv regression,0.848,0.78 Random splitting,qm8,NN regression(CM),0.753,0.699 Random splitting,qm8,DTNN,0.842,0.754 Random splitting,qm8,MPNN,0.828,0.764 Stratified splitting,qm8,Random forest,0.971,0.69 Stratified splitting,qm8,NN regression,0.934,0.712 Stratified splitting,qm8,Graphconv regression,0.846,0.767 Stratified splitting,qm8,NN regression(CM),0.761,0.696 Stratified splitting,qm8,DTNN,0.846,0.745 Stratified splitting,qm8,MPNN,0.839,0.752 Index splitting,qm9,NN regression,0.839,0.708 Index splitting,qm9,Graphconv regression,0.754,0.768 Index splitting,qm9,NN regression(CM),0.803,0.8 Loading
devtools/jenkins/jenkins.sh +15 −6 Original line number Diff line number Diff line Loading @@ -10,13 +10,22 @@ export DEEPCHEM_DATA_DIR='/tmp/molnet_'$envname mkdir $DEEPCHEM_DATA_DIR cd examples python benchmark.py -d tox21 -m weave -m graphconv -m tf_robust -m tf -m irv -m xgb -m logreg --seed 123 export retval_tox21=$? python benchmark.py -d hiv -m graphconv -m tf -m irv -m logreg --seed 123 export retval_hiv=$? python benchmark.py -d clintox -m weave -m graphconv -m tf -m tf_robust -m irv -m xgb -m logreg --seed 123 python benchmark.py -d sider -m weave -m graphconv -m tf -m tf_robust -m irv -m logreg --seed 123 python benchmark.py -d bbbp -m weave -m graphconv -m tf -m irv -m logreg --seed 123 python benchmark.py -d hiv -m weave -m graphconv -m tf -m irv -m logreg --seed 123 python benchmark.py -d muv -m weave -m graphconv -m tf -m tf_robust -m irv -m logreg --seed 123 python benchmark.py -d bace -m weave -m graphconv -m tf -m irv -m logreg --seed 123 python benchmark.py -d delaney -m weave_regression -m graphconvreg -m tf_regression -m dag_regression -m mpnn --seed 123 export retval_delaney=$? python benchmark.py -d qm7 -m dtnn -m graphconvreg -m tf_regression_ft --seed 123 export retval_qm7=$? python benchmark.py -d sampl -m weave_regression -m graphconvreg -m tf_regression -m dag_regression -m mpnn --seed 123 python benchmark.py -d lipo -m weave_regression -m graphconvreg -m tf_regression -m dag_regression -m mpnn --seed 123 python benchmark.py -d qm7 -m dtnn -m graphconvreg -m tf_regression_ft -m tf_regression -s random -s stratified --seed 123 python benchmark.py -d qm7b -m dtnn -m tf_regression_ft --seed 123 python benchmark.py -d qm8 -m dtnn -m mpnn -m graphconvreg -m tf_regression -m tf_regression_ft --seed 123 python benchmark.py -d qm9 -m dtnn -m graphconvreg -m tf_regression -m tf_regression_ft --seed 123 cd .. nosetests -v devtools/jenkins/compare_results.py --with-xunit || true Loading
