Loading devtools/jenkins/desired_results_backup.csvdeleted 100644 → 0 +0 −152 Original line number Original line Diff line number Diff line split,dataset,model,Train score/ROC-AUC,Valid score/ROC-AUC Index splitting,tox21,Logistic regression,0.903,0.705 Index splitting,tox21,Random forest,0.999,0.733 Index splitting,tox21,IRV,0.811,0.767 Index splitting,tox21,NN classification,0.856,0.763 Index splitting,tox21,Robust NN,0.857,0.767 Index splitting,tox21,Graph convolution,0.872,0.798 Index splitting,muv,Logistic regression,0.963,0.766 Index splitting,muv,NN classification,0.904,0.764 Index splitting,muv,Robust NN,0.934,0.781 Index splitting,muv,Graph convolution,0.84,0.823 Index splitting,pcba,Logistic regression,0.809,0.776 Index splitting,pcba,NN classification,0.826,0.802 Index splitting,pcba,Robust NN,0.809,0.783 Index splitting,pcba,Graph convolution,0.876,0.852 Index splitting,sider,Logistic regression,0.933,0.62 Index splitting,sider,Random forest,0.999,0.67 Index splitting,sider,IRV,0.649,0.642 Index splitting,sider,NN classification,0.775,0.634 Index splitting,sider,Robust NN,0.803,0.632 Index splitting,sider,Graph convolution,0.708,0.594 Index splitting,toxcast,Logistic regression,0.721,0.575 Index splitting,toxcast,NN classification,0.83,0.678 Index splitting,toxcast,Robust NN,0.825,0.68 Index splitting,toxcast,Graph convolution,0.821,0.72 Index splitting,clintox,Logistic regression,0.967,0.676 Index splitting,clintox,Random forest,0.995,0.776 Index splitting,clintox,IRV,0.763,0.814 Index splitting,clintox,NN classification,0.934,0.83 Index splitting,clintox,Robust NN,0.949,0.827 Index splitting,clintox,Graph convolution,0.946,0.86 Index splitting,hiv,Logistic regression,0.864,0.739 Index splitting,hiv,Random forest,0.999,0.72 Index splitting,hiv,IRV,0.841,0.724 Index splitting,hiv,NN classification,0.761,0.652 Index splitting,hiv,Robust NN,0.78,0.708 Index splitting,hiv,Graph convolution,0.876,0.779 Random splitting,tox21,Logistic regression,0.902,0.715 Random splitting,tox21,Random forest,0.999,0.764 Random splitting,tox21,IRV,0.808,0.767 Random splitting,tox21,NN classification,0.844,0.777 Random splitting,tox21,Robust NN,0.855,0.773 Random splitting,tox21,Graph convolution,0.865,0.827 Random splitting,muv,Logistic regression,0.957,0.719 Random splitting,muv,NN classification,0.902,0.734 Random splitting,muv,Robust NN,0.933,0.732 Random splitting,muv,Graph convolution,0.86,0.73 Random splitting,pcba,Logistic regression,0.808,0.776 Random splitting,pcba,NN classification,0.811,0.778 Random splitting,pcba,Robust NN,0.811,0.771 Random splitting,pcba,Graph convolution,0.872,0.844 Random splitting,sider,Logistic regression,0.929,0.656 Random splitting,sider,Random forest,0.999,0.665 Random splitting,sider,IRV,0.648,0.596 Random splitting,sider,NN classification,0.777,0.655 Random splitting,sider,Robust NN,0.804,0.63 Random splitting,sider,Graph convolution,0.705,0.618 Random splitting,toxcast,Logistic regression,0.725,0.586 Random splitting,toxcast,NN classification,0.836,0.684 Random splitting,toxcast,Robust NN,0.822,0.681 Random splitting,toxcast,Graph convolution,0.82,0.717 Random splitting,clintox,Logistic regression,0.972,0.725 Random splitting,clintox,Random forest,0.997,0.67 Random splitting,clintox,IRV,0.809,0.846 Random splitting,clintox,NN classification,0.951,0.834 Random splitting,clintox,Robust NN,0.959,0.83 Random splitting,clintox,Graph convolution,0.975,0.876 Random splitting,hiv,Logistic regression,0.86,0.806 Random splitting,hiv,Random forest,0.999,0.85 Random splitting,hiv,IRV,0.839,0.809 Random splitting,hiv,NN classification,0.742,0.715 Random splitting,hiv,Robust NN,0.753,0.727 Random splitting,hiv,Graph convolution,0.847,0.803 Scaffold splitting,tox21,Logistic regression,0.9,0.65 Scaffold splitting,tox21,Random forest,0.999,0.629 Scaffold splitting,tox21,IRV,0.823,0.708 Scaffold splitting,tox21,NN classification,0.863,0.703 Scaffold splitting,tox21,Robust NN,0.861,0.71 Scaffold splitting,tox21,Graph convolution,0.885,0.732 Scaffold splitting,muv,Logistic regression,0.947,0.767 Scaffold splitting,muv,NN classification,0.899,0.762 Scaffold splitting,muv,Robust NN,0.944,0.726 Scaffold splitting,muv,Graph convolution,0.872,0.795 Scaffold splitting,pcba,Logistic regression,0.81,0.742 Scaffold splitting,pcba,NN classification,0.814,0.76 Scaffold splitting,pcba,Robust NN,0.812,0.756 Scaffold splitting,pcba,Graph convolution,0.874,0.817 Scaffold splitting,sider,Logistic regression,0.926,0.592 Scaffold splitting,sider,Random forest,0.999,0.619 Scaffold splitting,sider,IRV,0.639,0.599 Scaffold splitting,sider,NN classification,0.776,0.557 Scaffold splitting,sider,Robust NN,0.797,0.56 Scaffold splitting,sider,Graph convolution,0.722,0.583 Scaffold splitting,toxcast,Logistic regression,0.716,0.492 Scaffold splitting,toxcast,NN classification,0.828,0.617 Scaffold splitting,toxcast,Robust NN,0.83,0.614 Scaffold splitting,toxcast,Graph convolution,0.832,0.638 Scaffold splitting,clintox,Logistic regression,0.96,0.803 Scaffold splitting,clintox,Random forest,0.993,0.735 Scaffold splitting,clintox,IRV,0.793,0.718 Scaffold splitting,clintox,NN classification,0.947,0.862 Scaffold splitting,clintox,Robust NN,0.953,0.89 Scaffold splitting,clintox,Graph convolution,0.957,0.823 Scaffold splitting,hiv,Logistic regression,0.858,0.798 Scaffold splitting,hiv,Random forest,0.946,0.562 Scaffold splitting,hiv,IRV,0.847,0.811 Scaffold splitting,hiv,NN classification,0.775,0.765 Scaffold splitting,hiv,Robust NN,0.785,0.748 Scaffold splitting,hiv,Graph convolution,0.867,0.769 Index splitting,delaney,Random forest,0.953,0.626 Index splitting,delaney,NN regression,0.868,0.578 Index splitting,delaney,Graphconv regression,0.967,0.79 Random splitting,delaney,Random forest,0.951,0.684 Random splitting,delaney,NN regression,0.865,0.574 Random splitting,delaney,Graphconv regression,0.964,0.782 Scaffold splitting,delaney,Random forest,0.953,0.284 Scaffold splitting,delaney,NN regression,0.866,0.342 Scaffold splitting,delaney,Graphconv regression,0.967,0.606 Index splitting,sampl,Random forest,0.968,0.736 Index splitting,sampl,NN regression,0.917,0.764 Index splitting,sampl,Graphconv regression,0.982,0.864 Random splitting,sampl,Random forest,0.967,0.752 Random splitting,sampl,NN regression,0.908,0.83 Random splitting,sampl,Graphconv regression,0.987,0.868 Scaffold splitting,sampl,Random forest,0.966,0.473 Scaffold splitting,sampl,NN regression,0.891,0.217 Scaffold splitting,sampl,Graphconv regression,0.985,0.666 Index splitting,nci,NN regression,0.171,0.062 Index splitting,nci,Graphconv regression,0.123,0.048 Random splitting,nci,NN regression,0.168,0.085 Random splitting,nci,Graphconv regression,0.117,0.076 Scaffold splitting,nci,NN regression,0.18,0.052 Scaffold splitting,nci,Graphconv regression,0.131,0.046 Random splitting,pdbbind(core),Random forest,0.969,0.445 Random splitting,pdbbind(core),NN regression,0.973,0.494 Random splitting,pdbbind(refined),Random forest,0.963,0.511 Random splitting,pdbbind(refined),NN regression,0.987,0.503 Random splitting,pdbbind(full),Random forest,0.965,0.493 Random splitting,pdbbind(full),NN regression,0.983,0.528 Index splitting,chembl,NN regression,0.443,0.427 Random splitting,chembl,NN regression,0.464,0.434 Scaffold splitting,chembl,NN regression,0.484,0.361 Index splitting,qm7,NN regression(CM),0.997,0.986 Random splitting,qm7,NN regression(CM),0.999,0.999 Stratified splitting,qm7,NN regression(CM),0.999,0.999 Index splitting,qm7b,NN regression(CM),0.931,0.803 Random splitting,qm7b,NN regression(CM),0.923,0.884 Stratified splitting,qm7b,NN regression(CM),0.934,0.884 Index splitting,qm9,NN regression(CM),0.733,0.791 Random splitting,qm9,NN regression(CM),0.811,0.823 Stratified splitting,qm9,NN regression(CM),0.843,0.818 User-defined splitting,kaggle,NN regression,0.748,0.452 Loading
devtools/jenkins/desired_results_backup.csvdeleted 100644 → 0 +0 −152 Original line number Original line Diff line number Diff line split,dataset,model,Train score/ROC-AUC,Valid score/ROC-AUC Index splitting,tox21,Logistic regression,0.903,0.705 Index splitting,tox21,Random forest,0.999,0.733 Index splitting,tox21,IRV,0.811,0.767 Index splitting,tox21,NN classification,0.856,0.763 Index splitting,tox21,Robust NN,0.857,0.767 Index splitting,tox21,Graph convolution,0.872,0.798 Index splitting,muv,Logistic regression,0.963,0.766 Index splitting,muv,NN classification,0.904,0.764 Index splitting,muv,Robust NN,0.934,0.781 Index splitting,muv,Graph convolution,0.84,0.823 Index splitting,pcba,Logistic regression,0.809,0.776 Index splitting,pcba,NN classification,0.826,0.802 Index splitting,pcba,Robust NN,0.809,0.783 Index splitting,pcba,Graph convolution,0.876,0.852 Index splitting,sider,Logistic regression,0.933,0.62 Index splitting,sider,Random forest,0.999,0.67 Index splitting,sider,IRV,0.649,0.642 Index splitting,sider,NN classification,0.775,0.634 Index splitting,sider,Robust NN,0.803,0.632 Index splitting,sider,Graph convolution,0.708,0.594 Index splitting,toxcast,Logistic regression,0.721,0.575 Index splitting,toxcast,NN classification,0.83,0.678 Index splitting,toxcast,Robust NN,0.825,0.68 Index splitting,toxcast,Graph convolution,0.821,0.72 Index splitting,clintox,Logistic regression,0.967,0.676 Index splitting,clintox,Random forest,0.995,0.776 Index splitting,clintox,IRV,0.763,0.814 Index splitting,clintox,NN classification,0.934,0.83 Index splitting,clintox,Robust NN,0.949,0.827 Index splitting,clintox,Graph convolution,0.946,0.86 Index splitting,hiv,Logistic regression,0.864,0.739 Index splitting,hiv,Random forest,0.999,0.72 Index splitting,hiv,IRV,0.841,0.724 Index splitting,hiv,NN classification,0.761,0.652 Index splitting,hiv,Robust NN,0.78,0.708 Index splitting,hiv,Graph convolution,0.876,0.779 Random splitting,tox21,Logistic regression,0.902,0.715 Random splitting,tox21,Random forest,0.999,0.764 Random splitting,tox21,IRV,0.808,0.767 Random splitting,tox21,NN classification,0.844,0.777 Random splitting,tox21,Robust NN,0.855,0.773 Random splitting,tox21,Graph convolution,0.865,0.827 Random splitting,muv,Logistic regression,0.957,0.719 Random splitting,muv,NN classification,0.902,0.734 Random splitting,muv,Robust NN,0.933,0.732 Random splitting,muv,Graph convolution,0.86,0.73 Random splitting,pcba,Logistic regression,0.808,0.776 Random splitting,pcba,NN classification,0.811,0.778 Random splitting,pcba,Robust NN,0.811,0.771 Random splitting,pcba,Graph convolution,0.872,0.844 Random splitting,sider,Logistic regression,0.929,0.656 Random splitting,sider,Random forest,0.999,0.665 Random splitting,sider,IRV,0.648,0.596 Random splitting,sider,NN classification,0.777,0.655 Random splitting,sider,Robust NN,0.804,0.63 Random splitting,sider,Graph convolution,0.705,0.618 Random splitting,toxcast,Logistic regression,0.725,0.586 Random splitting,toxcast,NN classification,0.836,0.684 Random splitting,toxcast,Robust NN,0.822,0.681 Random splitting,toxcast,Graph convolution,0.82,0.717 Random splitting,clintox,Logistic regression,0.972,0.725 Random splitting,clintox,Random forest,0.997,0.67 Random splitting,clintox,IRV,0.809,0.846 Random splitting,clintox,NN classification,0.951,0.834 Random splitting,clintox,Robust NN,0.959,0.83 Random splitting,clintox,Graph convolution,0.975,0.876 Random splitting,hiv,Logistic regression,0.86,0.806 Random splitting,hiv,Random forest,0.999,0.85 Random splitting,hiv,IRV,0.839,0.809 Random splitting,hiv,NN classification,0.742,0.715 Random splitting,hiv,Robust NN,0.753,0.727 Random splitting,hiv,Graph convolution,0.847,0.803 Scaffold splitting,tox21,Logistic regression,0.9,0.65 Scaffold splitting,tox21,Random forest,0.999,0.629 Scaffold splitting,tox21,IRV,0.823,0.708 Scaffold splitting,tox21,NN classification,0.863,0.703 Scaffold splitting,tox21,Robust NN,0.861,0.71 Scaffold splitting,tox21,Graph convolution,0.885,0.732 Scaffold splitting,muv,Logistic regression,0.947,0.767 Scaffold splitting,muv,NN classification,0.899,0.762 Scaffold splitting,muv,Robust NN,0.944,0.726 Scaffold splitting,muv,Graph convolution,0.872,0.795 Scaffold splitting,pcba,Logistic regression,0.81,0.742 Scaffold splitting,pcba,NN classification,0.814,0.76 Scaffold splitting,pcba,Robust NN,0.812,0.756 Scaffold splitting,pcba,Graph convolution,0.874,0.817 Scaffold splitting,sider,Logistic regression,0.926,0.592 Scaffold splitting,sider,Random forest,0.999,0.619 Scaffold splitting,sider,IRV,0.639,0.599 Scaffold splitting,sider,NN classification,0.776,0.557 Scaffold splitting,sider,Robust NN,0.797,0.56 Scaffold splitting,sider,Graph convolution,0.722,0.583 Scaffold splitting,toxcast,Logistic regression,0.716,0.492 Scaffold splitting,toxcast,NN classification,0.828,0.617 Scaffold splitting,toxcast,Robust NN,0.83,0.614 Scaffold splitting,toxcast,Graph convolution,0.832,0.638 Scaffold splitting,clintox,Logistic regression,0.96,0.803 Scaffold splitting,clintox,Random forest,0.993,0.735 Scaffold splitting,clintox,IRV,0.793,0.718 Scaffold splitting,clintox,NN classification,0.947,0.862 Scaffold splitting,clintox,Robust NN,0.953,0.89 Scaffold splitting,clintox,Graph convolution,0.957,0.823 Scaffold splitting,hiv,Logistic regression,0.858,0.798 Scaffold splitting,hiv,Random forest,0.946,0.562 Scaffold splitting,hiv,IRV,0.847,0.811 Scaffold splitting,hiv,NN classification,0.775,0.765 Scaffold splitting,hiv,Robust NN,0.785,0.748 Scaffold splitting,hiv,Graph convolution,0.867,0.769 Index splitting,delaney,Random forest,0.953,0.626 Index splitting,delaney,NN regression,0.868,0.578 Index splitting,delaney,Graphconv regression,0.967,0.79 Random splitting,delaney,Random forest,0.951,0.684 Random splitting,delaney,NN regression,0.865,0.574 Random splitting,delaney,Graphconv regression,0.964,0.782 Scaffold splitting,delaney,Random forest,0.953,0.284 Scaffold splitting,delaney,NN regression,0.866,0.342 Scaffold splitting,delaney,Graphconv regression,0.967,0.606 Index splitting,sampl,Random forest,0.968,0.736 Index splitting,sampl,NN regression,0.917,0.764 Index splitting,sampl,Graphconv regression,0.982,0.864 Random splitting,sampl,Random forest,0.967,0.752 Random splitting,sampl,NN regression,0.908,0.83 Random splitting,sampl,Graphconv regression,0.987,0.868 Scaffold splitting,sampl,Random forest,0.966,0.473 Scaffold splitting,sampl,NN regression,0.891,0.217 Scaffold splitting,sampl,Graphconv regression,0.985,0.666 Index splitting,nci,NN regression,0.171,0.062 Index splitting,nci,Graphconv regression,0.123,0.048 Random splitting,nci,NN regression,0.168,0.085 Random splitting,nci,Graphconv regression,0.117,0.076 Scaffold splitting,nci,NN regression,0.18,0.052 Scaffold splitting,nci,Graphconv regression,0.131,0.046 Random splitting,pdbbind(core),Random forest,0.969,0.445 Random splitting,pdbbind(core),NN regression,0.973,0.494 Random splitting,pdbbind(refined),Random forest,0.963,0.511 Random splitting,pdbbind(refined),NN regression,0.987,0.503 Random splitting,pdbbind(full),Random forest,0.965,0.493 Random splitting,pdbbind(full),NN regression,0.983,0.528 Index splitting,chembl,NN regression,0.443,0.427 Random splitting,chembl,NN regression,0.464,0.434 Scaffold splitting,chembl,NN regression,0.484,0.361 Index splitting,qm7,NN regression(CM),0.997,0.986 Random splitting,qm7,NN regression(CM),0.999,0.999 Stratified splitting,qm7,NN regression(CM),0.999,0.999 Index splitting,qm7b,NN regression(CM),0.931,0.803 Random splitting,qm7b,NN regression(CM),0.923,0.884 Stratified splitting,qm7b,NN regression(CM),0.934,0.884 Index splitting,qm9,NN regression(CM),0.733,0.791 Random splitting,qm9,NN regression(CM),0.811,0.823 Stratified splitting,qm9,NN regression(CM),0.843,0.818 User-defined splitting,kaggle,NN regression,0.748,0.452
