diff --git a/atomsci/ddm/data/descriptor_sets_sources_by_descr_type.csv b/atomsci/ddm/data/descriptor_sets_sources_by_descr_type.csv index 787842ae..825930b1 100644 --- a/atomsci/ddm/data/descriptor_sets_sources_by_descr_type.csv +++ b/atomsci/ddm/data/descriptor_sets_sources_by_descr_type.csv @@ -9,3 +9,5 @@ moe_informative,ASA+;ASA-;ASA_H;ASA_P;ASA;BCUT_PEOE_0;BCUT_PEOE_1;BCUT_PEOE_2;BC mordred_raw,ABC;ABCGG;nAcid;nBase;SpAbs_A;SpMax_A;SpDiam_A;SpAD_A;SpMAD_A;LogEE_A;VE1_A;VE2_A;VE3_A;VR1_A;VR2_A;VR3_A;nAromAtom;nAromBond;nAtom;nHeavyAtom;nSpiro;nBridgehead;nHetero;nH;nB;nC;nN;nO;nS;nP;nF;nCl;nBr;nI;nX;ATS0dv;ATS1dv;ATS2dv;ATS3dv;ATS4dv;ATS5dv;ATS6dv;ATS7dv;ATS8dv;ATS0d;ATS1d;ATS2d;ATS3d;ATS4d;ATS5d;ATS6d;ATS7d;ATS8d;ATS0s;ATS1s;ATS2s;ATS3s;ATS4s;ATS5s;ATS6s;ATS7s;ATS8s;ATS0Z;ATS1Z;ATS2Z;ATS3Z;ATS4Z;ATS5Z;ATS6Z;ATS7Z;ATS8Z;ATS0m;ATS1m;ATS2m;ATS3m;ATS4m;ATS5m;ATS6m;ATS7m;ATS8m;ATS0v;ATS1v;ATS2v;ATS3v;ATS4v;ATS5v;ATS6v;ATS7v;ATS8v;ATS0se;ATS1se;ATS2se;ATS3se;ATS4se;ATS5se;ATS6se;ATS7se;ATS8se;ATS0pe;ATS1pe;ATS2pe;ATS3pe;ATS4pe;ATS5pe;ATS6pe;ATS7pe;ATS8pe;ATS0are;ATS1are;ATS2are;ATS3are;ATS4are;ATS5are;ATS6are;ATS7are;ATS8are;ATS0p;ATS1p;ATS2p;ATS3p;ATS4p;ATS5p;ATS6p;ATS7p;ATS8p;ATS0i;ATS1i;ATS2i;ATS3i;ATS4i;ATS5i;ATS6i;ATS7i;ATS8i;AATS0dv;AATS1dv;AATS2dv;AATS3dv;AATS4dv;AATS5dv;AATS6dv;AATS7dv;AATS8dv;AATS0d;AATS1d;AATS2d;AATS3d;AATS4d;AATS5d;AATS6d;AATS7d;AATS8d;AATS0s;AATS1s;AATS2s;AATS3s;AATS4s;AATS5s;AATS6s;AATS7s;AATS8s;AATS0Z;AATS1Z;AATS2Z;AATS3Z;AATS4Z;AATS5Z;AATS6Z;AATS7Z;AATS8Z;AATS0m;AATS1m;AATS2m;AATS3m;AATS4m;AATS5m;AATS6m;AATS7m;AATS8m;AATS0v;AATS1v;AATS2v;AATS3v;AATS4v;AATS5v;AATS6v;AATS7v;AATS8v;AATS0se;AATS1se;AATS2se;AATS3se;AATS4se;AATS5se;AATS6se;AATS7se;AATS8se;AATS0pe;AATS1pe;AATS2pe;AATS3pe;AATS4pe;AATS5pe;AATS6pe;AATS7pe;AATS8pe;AATS0are;AATS1are;AATS2are;AATS3are;AATS4are;AATS5are;AATS6are;AATS7are;AATS8are;AATS0p;AATS1p;AATS2p;AATS3p;AATS4p;AATS5p;AATS6p;AATS7p;AATS8p;AATS0i;AATS1i;AATS2i;AATS3i;AATS4i;AATS5i;AATS6i;AATS7i;AATS8i;ATSC0c;ATSC1c;ATSC2c;ATSC3c;ATSC4c;ATSC5c;ATSC6c;ATSC7c;ATSC8c;ATSC0dv;ATSC1dv;ATSC2dv;ATSC3dv;ATSC4dv;ATSC5dv;ATSC6dv;ATSC7dv;ATSC8dv;ATSC0d;ATSC1d;ATSC2d;ATSC3d;ATSC4d;ATSC5d;ATSC6d;ATSC7d;ATSC8d;ATSC0s;ATSC1s;ATSC2s;ATSC3s;ATSC4s;ATSC5s;ATSC6s;ATSC7s;ATSC8s;ATSC0Z;ATSC1Z;ATSC2Z;ATSC3Z;ATSC4Z;ATSC5Z;ATSC6Z;ATSC7Z;ATSC8Z;ATSC0m;ATSC1m;ATSC2m;ATSC3m;ATSC4m;ATSC5m;ATSC6m;ATSC7m;ATSC8m;ATSC0v;ATSC1v;ATSC2v;ATSC3v;ATSC4v;ATSC5v;ATSC6v;ATSC7v;ATSC8v;ATSC0se;ATSC1se;ATSC2se;ATSC3se;ATSC4se;ATSC5se;ATSC6se;ATSC7se;ATSC8se;ATSC0pe;ATSC1pe;ATSC2pe;ATSC3pe;ATSC4pe;ATSC5pe;ATSC6pe;ATSC7pe;ATSC8pe;ATSC0are;ATSC1are;ATSC2are;ATSC3are;ATSC4are;ATSC5are;ATSC6are;ATSC7are;ATSC8are;ATSC0p;ATSC1p;ATSC2p;ATSC3p;ATSC4p;ATSC5p;ATSC6p;ATSC7p;ATSC8p;ATSC0i;ATSC1i;ATSC2i;ATSC3i;ATSC4i;ATSC5i;ATSC6i;ATSC7i;ATSC8i;AATSC0c;AATSC1c;AATSC2c;AATSC3c;AATSC4c;AATSC5c;AATSC6c;AATSC7c;AATSC8c;AATSC0dv;AATSC1dv;AATSC2dv;AATSC3dv;AATSC4dv;AATSC5dv;AATSC6dv;AATSC7dv;AATSC8dv;AATSC0d;AATSC1d;AATSC2d;AATSC3d;AATSC4d;AATSC5d;AATSC6d;AATSC7d;AATSC8d;AATSC0s;AATSC1s;AATSC2s;AATSC3s;AATSC4s;AATSC5s;AATSC6s;AATSC7s;AATSC8s;AATSC0Z;AATSC1Z;AATSC2Z;AATSC3Z;AATSC4Z;AATSC5Z;AATSC6Z;AATSC7Z;AATSC8Z;AATSC0m;AATSC1m;AATSC2m;AATSC3m;AATSC4m;AATSC5m;AATSC6m;AATSC7m;AATSC8m;AATSC0v;AATSC1v;AATSC2v;AATSC3v;AATSC4v;AATSC5v;AATSC6v;AATSC7v;AATSC8v;AATSC0se;AATSC1se;AATSC2se;AATSC3se;AATSC4se;AATSC5se;AATSC6se;AATSC7se;AATSC8se;AATSC0pe;AATSC1pe;AATSC2pe;AATSC3pe;AATSC4pe;AATSC5pe;AATSC6pe;AATSC7pe;AATSC8pe;AATSC0are;AATSC1are;AATSC2are;AATSC3are;AATSC4are;AATSC5are;AATSC6are;AATSC7are;AATSC8are;AATSC0p;AATSC1p;AATSC2p;AATSC3p;AATSC4p;AATSC5p;AATSC6p;AATSC7p;AATSC8p;AATSC0i;AATSC1i;AATSC2i;AATSC3i;AATSC4i;AATSC5i;AATSC6i;AATSC7i;AATSC8i;MATS1c;MATS2c;MATS3c;MATS4c;MATS5c;MATS6c;MATS7c;MATS8c;MATS1dv;MATS2dv;MATS3dv;MATS4dv;MATS5dv;MATS6dv;MATS7dv;MATS8dv;MATS1d;MATS2d;MATS3d;MATS4d;MATS5d;MATS6d;MATS7d;MATS8d;MATS1s;MATS2s;MATS3s;MATS4s;MATS5s;MATS6s;MATS7s;MATS8s;MATS1Z;MATS2Z;MATS3Z;MATS4Z;MATS5Z;MATS6Z;MATS7Z;MATS8Z;MATS1m;MATS2m;MATS3m;MATS4m;MATS5m;MATS6m;MATS7m;MATS8m;MATS1v;MATS2v;MATS3v;MATS4v;MATS5v;MATS6v;MATS7v;MATS8v;MATS1se;MATS2se;MATS3se;MATS4se;MATS5se;MATS6se;MATS7se;MATS8se;MATS1pe;MATS2pe;MATS3pe;MATS4pe;MATS5pe;MATS6pe;MATS7pe;MATS8pe;MATS1are;MATS2are;MATS3are;MATS4are;MATS5are;MATS6are;MATS7are;MATS8are;MATS1p;MATS2p;MATS3p;MATS4p;MATS5p;MATS6p;MATS7p;MATS8p;MATS1i;MATS2i;MATS3i;MATS4i;MATS5i;MATS6i;MATS7i;MATS8i;GATS1c;GATS2c;GATS3c;GATS4c;GATS5c;GATS6c;GATS7c;GATS8c;GATS1dv;GATS2dv;GATS3dv;GATS4dv;GATS5dv;GATS6dv;GATS7dv;GATS8dv;GATS1d;GATS2d;GATS3d;GATS4d;GATS5d;GATS6d;GATS7d;GATS8d;GATS1s;GATS2s;GATS3s;GATS4s;GATS5s;GATS6s;GATS7s;GATS8s;GATS1Z;GATS2Z;GATS3Z;GATS4Z;GATS5Z;GATS6Z;GATS7Z;GATS8Z;GATS1m;GATS2m;GATS3m;GATS4m;GATS5m;GATS6m;GATS7m;GATS8m;GATS1v;GATS2v;GATS3v;GATS4v;GATS5v;GATS6v;GATS7v;GATS8v;GATS1se;GATS2se;GATS3se;GATS4se;GATS5se;GATS6se;GATS7se;GATS8se;GATS1pe;GATS2pe;GATS3pe;GATS4pe;GATS5pe;GATS6pe;GATS7pe;GATS8pe;GATS1are;GATS2are;GATS3are;GATS4are;GATS5are;GATS6are;GATS7are;GATS8are;GATS1p;GATS2p;GATS3p;GATS4p;GATS5p;GATS6p;GATS7p;GATS8p;GATS1i;GATS2i;GATS3i;GATS4i;GATS5i;GATS6i;GATS7i;GATS8i;BCUTc-1h;BCUTc-1l;BCUTdv-1h;BCUTdv-1l;BCUTd-1h;BCUTd-1l;BCUTs-1h;BCUTs-1l;BCUTZ-1h;BCUTZ-1l;BCUTm-1h;BCUTm-1l;BCUTv-1h;BCUTv-1l;BCUTse-1h;BCUTse-1l;BCUTpe-1h;BCUTpe-1l;BCUTare-1h;BCUTare-1l;BCUTp-1h;BCUTp-1l;BCUTi-1h;BCUTi-1l;BalabanJ;SpAbs_DzZ;SpMax_DzZ;SpDiam_DzZ;SpAD_DzZ;SpMAD_DzZ;LogEE_DzZ;SM1_DzZ;VE1_DzZ;VE2_DzZ;VE3_DzZ;VR1_DzZ;VR2_DzZ;VR3_DzZ;SpAbs_Dzm;SpMax_Dzm;SpDiam_Dzm;SpAD_Dzm;SpMAD_Dzm;LogEE_Dzm;SM1_Dzm;VE1_Dzm;VE2_Dzm;VE3_Dzm;VR1_Dzm;VR2_Dzm;VR3_Dzm;SpAbs_Dzv;SpMax_Dzv;SpDiam_Dzv;SpAD_Dzv;SpMAD_Dzv;LogEE_Dzv;SM1_Dzv;VE1_Dzv;VE2_Dzv;VE3_Dzv;VR1_Dzv;VR2_Dzv;VR3_Dzv;SpAbs_Dzse;SpMax_Dzse;SpDiam_Dzse;SpAD_Dzse;SpMAD_Dzse;LogEE_Dzse;SM1_Dzse;VE1_Dzse;VE2_Dzse;VE3_Dzse;VR1_Dzse;VR2_Dzse;VR3_Dzse;SpAbs_Dzpe;SpMax_Dzpe;SpDiam_Dzpe;SpAD_Dzpe;SpMAD_Dzpe;LogEE_Dzpe;SM1_Dzpe;VE1_Dzpe;VE2_Dzpe;VE3_Dzpe;VR1_Dzpe;VR2_Dzpe;VR3_Dzpe;SpAbs_Dzare;SpMax_Dzare;SpDiam_Dzare;SpAD_Dzare;SpMAD_Dzare;LogEE_Dzare;SM1_Dzare;VE1_Dzare;VE2_Dzare;VE3_Dzare;VR1_Dzare;VR2_Dzare;VR3_Dzare;SpAbs_Dzp;SpMax_Dzp;SpDiam_Dzp;SpAD_Dzp;SpMAD_Dzp;LogEE_Dzp;SM1_Dzp;VE1_Dzp;VE2_Dzp;VE3_Dzp;VR1_Dzp;VR2_Dzp;VR3_Dzp;SpAbs_Dzi;SpMax_Dzi;SpDiam_Dzi;SpAD_Dzi;SpMAD_Dzi;LogEE_Dzi;SM1_Dzi;VE1_Dzi;VE2_Dzi;VE3_Dzi;VR1_Dzi;VR2_Dzi;VR3_Dzi;BertzCT;nBonds;nBondsO;nBondsS;nBondsD;nBondsT;nBondsA;nBondsM;nBondsKS;nBondsKD;RNCG;RPCG;C1SP1;C2SP1;C1SP2;C2SP2;C3SP2;C1SP3;C2SP3;C3SP3;C4SP3;HybRatio;FCSP3;Xch-3d;Xch-4d;Xch-5d;Xch-6d;Xch-7d;Xch-3dv;Xch-4dv;Xch-5dv;Xch-6dv;Xch-7dv;Xc-3d;Xc-4d;Xc-5d;Xc-6d;Xc-3dv;Xc-4dv;Xc-5dv;Xc-6dv;Xpc-4d;Xpc-5d;Xpc-6d;Xpc-4dv;Xpc-5dv;Xpc-6dv;Xp-0d;Xp-1d;Xp-2d;Xp-3d;Xp-4d;Xp-5d;Xp-6d;Xp-7d;AXp-0d;AXp-1d;AXp-2d;AXp-3d;AXp-4d;AXp-5d;AXp-6d;AXp-7d;Xp-0dv;Xp-1dv;Xp-2dv;Xp-3dv;Xp-4dv;Xp-5dv;Xp-6dv;Xp-7dv;AXp-0dv;AXp-1dv;AXp-2dv;AXp-3dv;AXp-4dv;AXp-5dv;AXp-6dv;AXp-7dv;SZ;Sm;Sv;Sse;Spe;Sare;Sp;Si;MZ;Mm;Mv;Mse;Mpe;Mare;Mp;Mi;SpAbs_Dt;SpMax_Dt;SpDiam_Dt;SpAD_Dt;SpMAD_Dt;LogEE_Dt;SM1_Dt;VE1_Dt;VE2_Dt;VE3_Dt;VR1_Dt;VR2_Dt;VR3_Dt;DetourIndex;SpAbs_D;SpMax_D;SpDiam_D;SpAD_D;SpMAD_D;LogEE_D;VE1_D;VE2_D;VE3_D;VR1_D;VR2_D;VR3_D;NsLi;NssBe;NssssBe;NssBH;NsssB;NssssB;NsCH3;NdCH2;NssCH2;NtCH;NdsCH;NaaCH;NsssCH;NddC;NtsC;NdssC;NaasC;NaaaC;NssssC;NsNH3;NsNH2;NssNH2;NdNH;NssNH;NaaNH;NtN;NsssNH;NdsN;NaaN;NsssN;NddsN;NaasN;NssssN;NsOH;NdO;NssO;NaaO;NsF;NsSiH3;NssSiH2;NsssSiH;NssssSi;NsPH2;NssPH;NsssP;NdsssP;NsssssP;NsSH;NdS;NssS;NaaS;NdssS;NddssS;NsCl;NsGeH3;NssGeH2;NsssGeH;NssssGe;NsAsH2;NssAsH;NsssAs;NsssdAs;NsssssAs;NsSeH;NdSe;NssSe;NaaSe;NdssSe;NddssSe;NsBr;NsSnH3;NssSnH2;NsssSnH;NssssSn;NsI;NsPbH3;NssPbH2;NsssPbH;NssssPb;SsLi;SssBe;SssssBe;SssBH;SsssB;SssssB;SsCH3;SdCH2;SssCH2;StCH;SdsCH;SaaCH;SsssCH;SddC;StsC;SdssC;SaasC;SaaaC;SssssC;SsNH3;SsNH2;SssNH2;SdNH;SssNH;SaaNH;StN;SsssNH;SdsN;SaaN;SsssN;SddsN;SaasN;SssssN;SsOH;SdO;SssO;SaaO;SsF;SsSiH3;SssSiH2;SsssSiH;SssssSi;SsPH2;SssPH;SsssP;SdsssP;SsssssP;SsSH;SdS;SssS;SaaS;SdssS;SddssS;SsCl;SsGeH3;SssGeH2;SsssGeH;SssssGe;SsAsH2;SssAsH;SsssAs;SsssdAs;SsssssAs;SsSeH;SdSe;SssSe;SaaSe;SdssSe;SddssSe;SsBr;SsSnH3;SssSnH2;SsssSnH;SssssSn;SsI;SsPbH3;SssPbH2;SsssPbH;SssssPb;MAXsLi;MAXssBe;MAXssssBe;MAXssBH;MAXsssB;MAXssssB;MAXsCH3;MAXdCH2;MAXssCH2;MAXtCH;MAXdsCH;MAXaaCH;MAXsssCH;MAXddC;MAXtsC;MAXdssC;MAXaasC;MAXaaaC;MAXssssC;MAXsNH3;MAXsNH2;MAXssNH2;MAXdNH;MAXssNH;MAXaaNH;MAXtN;MAXsssNH;MAXdsN;MAXaaN;MAXsssN;MAXddsN;MAXaasN;MAXssssN;MAXsOH;MAXdO;MAXssO;MAXaaO;MAXsF;MAXsSiH3;MAXssSiH2;MAXsssSiH;MAXssssSi;MAXsPH2;MAXssPH;MAXsssP;MAXdsssP;MAXsssssP;MAXsSH;MAXdS;MAXssS;MAXaaS;MAXdssS;MAXddssS;MAXsCl;MAXsGeH3;MAXssGeH2;MAXsssGeH;MAXssssGe;MAXsAsH2;MAXssAsH;MAXsssAs;MAXsssdAs;MAXsssssAs;MAXsSeH;MAXdSe;MAXssSe;MAXaaSe;MAXdssSe;MAXddssSe;MAXsBr;MAXsSnH3;MAXssSnH2;MAXsssSnH;MAXssssSn;MAXsI;MAXsPbH3;MAXssPbH2;MAXsssPbH;MAXssssPb;MINsLi;MINssBe;MINssssBe;MINssBH;MINsssB;MINssssB;MINsCH3;MINdCH2;MINssCH2;MINtCH;MINdsCH;MINaaCH;MINsssCH;MINddC;MINtsC;MINdssC;MINaasC;MINaaaC;MINssssC;MINsNH3;MINsNH2;MINssNH2;MINdNH;MINssNH;MINaaNH;MINtN;MINsssNH;MINdsN;MINaaN;MINsssN;MINddsN;MINaasN;MINssssN;MINsOH;MINdO;MINssO;MINaaO;MINsF;MINsSiH3;MINssSiH2;MINsssSiH;MINssssSi;MINsPH2;MINssPH;MINsssP;MINdsssP;MINsssssP;MINsSH;MINdS;MINssS;MINaaS;MINdssS;MINddssS;MINsCl;MINsGeH3;MINssGeH2;MINsssGeH;MINssssGe;MINsAsH2;MINssAsH;MINsssAs;MINsssdAs;MINsssssAs;MINsSeH;MINdSe;MINssSe;MINaaSe;MINdssSe;MINddssSe;MINsBr;MINsSnH3;MINssSnH2;MINsssSnH;MINssssSn;MINsI;MINsPbH3;MINssPbH2;MINsssPbH;MINssssPb;ECIndex;ETA_alpha;AETA_alpha;ETA_shape_p;ETA_shape_y;ETA_shape_x;ETA_beta;AETA_beta;ETA_beta_s;AETA_beta_s;ETA_beta_ns;AETA_beta_ns;ETA_beta_ns_d;AETA_beta_ns_d;ETA_eta;AETA_eta;ETA_eta_L;AETA_eta_L;ETA_eta_R;AETA_eta_R;ETA_eta_RL;AETA_eta_RL;ETA_eta_F;AETA_eta_F;ETA_eta_FL;AETA_eta_FL;ETA_eta_B;AETA_eta_B;ETA_eta_BR;AETA_eta_BR;ETA_dAlpha_A;ETA_dAlpha_B;ETA_epsilon_1;ETA_epsilon_2;ETA_epsilon_3;ETA_epsilon_4;ETA_epsilon_5;ETA_dEpsilon_A;ETA_dEpsilon_B;ETA_dEpsilon_C;ETA_dEpsilon_D;ETA_dBeta;AETA_dBeta;ETA_psi_1;ETA_dPsi_A;ETA_dPsi_B;fragCpx;fMF;nHBAcc;nHBDon;IC0;IC1;IC2;IC3;IC4;IC5;TIC0;TIC1;TIC2;TIC3;TIC4;TIC5;SIC0;SIC1;SIC2;SIC3;SIC4;SIC5;BIC0;BIC1;BIC2;BIC3;BIC4;BIC5;CIC0;CIC1;CIC2;CIC3;CIC4;CIC5;MIC0;MIC1;MIC2;MIC3;MIC4;MIC5;ZMIC0;ZMIC1;ZMIC2;ZMIC3;ZMIC4;ZMIC5;Kier1;Kier2;Kier3;Lipinski;GhoseFilter;FilterItLogS;VMcGowan;LabuteASA;PEOE_VSA1;PEOE_VSA2;PEOE_VSA3;PEOE_VSA4;PEOE_VSA5;PEOE_VSA6;PEOE_VSA7;PEOE_VSA8;PEOE_VSA9;PEOE_VSA10;PEOE_VSA11;PEOE_VSA12;PEOE_VSA13;SMR_VSA1;SMR_VSA2;SMR_VSA3;SMR_VSA4;SMR_VSA5;SMR_VSA6;SMR_VSA7;SMR_VSA8;SMR_VSA9;SlogP_VSA1;SlogP_VSA2;SlogP_VSA3;SlogP_VSA4;SlogP_VSA5;SlogP_VSA6;SlogP_VSA7;SlogP_VSA8;SlogP_VSA9;SlogP_VSA10;SlogP_VSA11;EState_VSA1;EState_VSA2;EState_VSA3;EState_VSA4;EState_VSA5;EState_VSA6;EState_VSA7;EState_VSA8;EState_VSA9;EState_VSA10;VSA_EState1;VSA_EState2;VSA_EState3;VSA_EState4;VSA_EState5;VSA_EState6;VSA_EState7;VSA_EState8;VSA_EState9;MDEC-11;MDEC-12;MDEC-13;MDEC-14;MDEC-22;MDEC-23;MDEC-24;MDEC-33;MDEC-34;MDEC-44;MDEO-11;MDEO-12;MDEO-22;MDEN-11;MDEN-12;MDEN-13;MDEN-22;MDEN-23;MDEN-33;MID;AMID;MID_h;AMID_h;MID_C;AMID_C;MID_N;AMID_N;MID_O;AMID_O;MID_X;AMID_X;MPC2;MPC3;MPC4;MPC5;MPC6;MPC7;MPC8;MPC9;MPC10;TMPC10;piPC1;piPC2;piPC3;piPC4;piPC5;piPC6;piPC7;piPC8;piPC9;piPC10;TpiPC10;apol;bpol;nRing;n3Ring;n4Ring;n5Ring;n6Ring;n7Ring;n8Ring;n9Ring;n10Ring;n11Ring;n12Ring;nG12Ring;nHRing;n3HRing;n4HRing;n5HRing;n6HRing;n7HRing;n8HRing;n9HRing;n10HRing;n11HRing;n12HRing;nG12HRing;naRing;n3aRing;n4aRing;n5aRing;n6aRing;n7aRing;n8aRing;n9aRing;n10aRing;n11aRing;n12aRing;nG12aRing;naHRing;n3aHRing;n4aHRing;n5aHRing;n6aHRing;n7aHRing;n8aHRing;n9aHRing;n10aHRing;n11aHRing;n12aHRing;nG12aHRing;nARing;n3ARing;n4ARing;n5ARing;n6ARing;n7ARing;n8ARing;n9ARing;n10ARing;n11ARing;n12ARing;nG12ARing;nAHRing;n3AHRing;n4AHRing;n5AHRing;n6AHRing;n7AHRing;n8AHRing;n9AHRing;n10AHRing;n11AHRing;n12AHRing;nG12AHRing;nFRing;n4FRing;n5FRing;n6FRing;n7FRing;n8FRing;n9FRing;n10FRing;n11FRing;n12FRing;nG12FRing;nFHRing;n4FHRing;n5FHRing;n6FHRing;n7FHRing;n8FHRing;n9FHRing;n10FHRing;n11FHRing;n12FHRing;nG12FHRing;nFaRing;n4FaRing;n5FaRing;n6FaRing;n7FaRing;n8FaRing;n9FaRing;n10FaRing;n11FaRing;n12FaRing;nG12FaRing;nFaHRing;n4FaHRing;n5FaHRing;n6FaHRing;n7FaHRing;n8FaHRing;n9FaHRing;n10FaHRing;n11FaHRing;n12FaHRing;nG12FaHRing;nFARing;n4FARing;n5FARing;n6FARing;n7FARing;n8FARing;n9FARing;n10FARing;n11FARing;n12FARing;nG12FARing;nFAHRing;n4FAHRing;n5FAHRing;n6FAHRing;n7FAHRing;n8FAHRing;n9FAHRing;n10FAHRing;n11FAHRing;n12FAHRing;nG12FAHRing;nRot;RotRatio;SLogP;SMR;TopoPSA(NO);TopoPSA;GGI1;GGI2;GGI3;GGI4;GGI5;GGI6;GGI7;GGI8;GGI9;GGI10;JGI1;JGI2;JGI3;JGI4;JGI5;JGI6;JGI7;JGI8;JGI9;JGI10;JGT10;Diameter;Radius;TopoShapeIndex;PetitjeanIndex;Vabc;VAdjMat;MWC01;MWC02;MWC03;MWC04;MWC05;MWC06;MWC07;MWC08;MWC09;MWC10;TMWC10;SRW02;SRW03;SRW04;SRW05;SRW06;SRW07;SRW08;SRW09;SRW10;TSRW10;MW;AMW;WPath;WPol;Zagreb1;Zagreb2;mZagreb1;mZagreb2,0,mordred mordred_filtered,AATS0Z;AATS0are;AATS0d;AATS0dv;AATS0i;AATS0m;AATS0p;AATS0pe;AATS0s;AATS0se;AATS0v;AATS1Z;AATS1are;AATS1d;AATS1dv;AATS1i;AATS1m;AATS1p;AATS1pe;AATS1s;AATS1se;AATS1v;AATS2Z;AATS2are;AATS2d;AATS2dv;AATS2i;AATS2m;AATS2p;AATS2pe;AATS2s;AATS2se;AATS2v;AATS3Z;AATS3are;AATS3d;AATS3dv;AATS3i;AATS3m;AATS3p;AATS3pe;AATS3s;AATS3se;AATS3v;AATS4Z;AATS4are;AATS4d;AATS4dv;AATS4i;AATS4m;AATS4p;AATS4pe;AATS4s;AATS4se;AATS4v;AATS5Z;AATS5are;AATS5d;AATS5dv;AATS5i;AATS5m;AATS5p;AATS5pe;AATS5s;AATS5se;AATS5v;AATS6Z;AATS6are;AATS6d;AATS6dv;AATS6i;AATS6m;AATS6p;AATS6pe;AATS6s;AATS6se;AATS6v;AATS7Z;AATS7are;AATS7d;AATS7dv;AATS7i;AATS7m;AATS7p;AATS7pe;AATS7s;AATS7se;AATS7v;AATS8Z;AATS8are;AATS8d;AATS8dv;AATS8i;AATS8m;AATS8p;AATS8pe;AATS8s;AATS8se;AATS8v;AATSC0Z;AATSC0are;AATSC0c;AATSC0d;AATSC0dv;AATSC0i;AATSC0m;AATSC0p;AATSC0pe;AATSC0s;AATSC0se;AATSC0v;AATSC1Z;AATSC1are;AATSC1c;AATSC1d;AATSC1dv;AATSC1i;AATSC1m;AATSC1p;AATSC1pe;AATSC1s;AATSC1se;AATSC1v;AATSC2Z;AATSC2are;AATSC2c;AATSC2d;AATSC2dv;AATSC2i;AATSC2m;AATSC2p;AATSC2pe;AATSC2s;AATSC2se;AATSC2v;AATSC3Z;AATSC3are;AATSC3c;AATSC3d;AATSC3dv;AATSC3i;AATSC3m;AATSC3p;AATSC3pe;AATSC3s;AATSC3se;AATSC3v;AATSC4Z;AATSC4are;AATSC4c;AATSC4d;AATSC4dv;AATSC4i;AATSC4m;AATSC4p;AATSC4pe;AATSC4s;AATSC4se;AATSC4v;AATSC5Z;AATSC5are;AATSC5c;AATSC5d;AATSC5dv;AATSC5i;AATSC5m;AATSC5p;AATSC5pe;AATSC5s;AATSC5se;AATSC5v;AATSC6Z;AATSC6are;AATSC6c;AATSC6d;AATSC6dv;AATSC6i;AATSC6m;AATSC6p;AATSC6pe;AATSC6s;AATSC6se;AATSC6v;AATSC7Z;AATSC7are;AATSC7c;AATSC7d;AATSC7dv;AATSC7i;AATSC7m;AATSC7p;AATSC7pe;AATSC7s;AATSC7se;AATSC7v;AATSC8Z;AATSC8are;AATSC8c;AATSC8d;AATSC8dv;AATSC8i;AATSC8m;AATSC8p;AATSC8pe;AATSC8s;AATSC8se;AATSC8v;ABC;ABCGG;AETA_alpha;AETA_beta;AETA_beta_ns;AETA_beta_ns_d;AETA_beta_s;AETA_dBeta;AETA_eta;AETA_eta_B;AETA_eta_BR;AETA_eta_F;AETA_eta_FL;AETA_eta_L;AETA_eta_R;AETA_eta_RL;AMID;AMID_C;AMID_N;AMID_O;AMID_X;AMID_h;AMW;ATS0Z;ATS0are;ATS0d;ATS0dv;ATS0i;ATS0m;ATS0p;ATS0pe;ATS0s;ATS0se;ATS0v;ATS1Z;ATS1are;ATS1d;ATS1dv;ATS1i;ATS1m;ATS1p;ATS1pe;ATS1s;ATS1se;ATS1v;ATS2Z;ATS2are;ATS2d;ATS2dv;ATS2i;ATS2m;ATS2p;ATS2pe;ATS2s;ATS2se;ATS2v;ATS3Z;ATS3are;ATS3d;ATS3dv;ATS3i;ATS3m;ATS3p;ATS3pe;ATS3s;ATS3se;ATS3v;ATS4Z;ATS4are;ATS4d;ATS4dv;ATS4i;ATS4m;ATS4p;ATS4pe;ATS4s;ATS4se;ATS4v;ATS5Z;ATS5are;ATS5d;ATS5dv;ATS5i;ATS5m;ATS5p;ATS5pe;ATS5s;ATS5se;ATS5v;ATS6Z;ATS6are;ATS6d;ATS6dv;ATS6i;ATS6m;ATS6p;ATS6pe;ATS6s;ATS6se;ATS6v;ATS7Z;ATS7are;ATS7d;ATS7dv;ATS7i;ATS7m;ATS7p;ATS7pe;ATS7s;ATS7se;ATS7v;ATS8Z;ATS8are;ATS8d;ATS8dv;ATS8i;ATS8m;ATS8p;ATS8pe;ATS8s;ATS8se;ATS8v;ATSC0Z;ATSC0are;ATSC0c;ATSC0d;ATSC0dv;ATSC0i;ATSC0m;ATSC0p;ATSC0pe;ATSC0s;ATSC0se;ATSC0v;ATSC1Z;ATSC1are;ATSC1c;ATSC1d;ATSC1dv;ATSC1i;ATSC1m;ATSC1p;ATSC1pe;ATSC1s;ATSC1se;ATSC1v;ATSC2Z;ATSC2are;ATSC2c;ATSC2d;ATSC2dv;ATSC2i;ATSC2m;ATSC2p;ATSC2pe;ATSC2s;ATSC2se;ATSC2v;ATSC3Z;ATSC3are;ATSC3c;ATSC3d;ATSC3dv;ATSC3i;ATSC3m;ATSC3p;ATSC3pe;ATSC3s;ATSC3se;ATSC3v;ATSC4Z;ATSC4are;ATSC4c;ATSC4d;ATSC4dv;ATSC4i;ATSC4m;ATSC4p;ATSC4pe;ATSC4s;ATSC4se;ATSC4v;ATSC5Z;ATSC5are;ATSC5c;ATSC5d;ATSC5dv;ATSC5i;ATSC5m;ATSC5p;ATSC5pe;ATSC5s;ATSC5se;ATSC5v;ATSC6Z;ATSC6are;ATSC6c;ATSC6d;ATSC6dv;ATSC6i;ATSC6m;ATSC6p;ATSC6pe;ATSC6s;ATSC6se;ATSC6v;ATSC7Z;ATSC7are;ATSC7c;ATSC7d;ATSC7dv;ATSC7i;ATSC7m;ATSC7p;ATSC7pe;ATSC7s;ATSC7se;ATSC7v;ATSC8Z;ATSC8are;ATSC8c;ATSC8d;ATSC8dv;ATSC8i;ATSC8m;ATSC8p;ATSC8pe;ATSC8s;ATSC8se;ATSC8v;AXp-0d;AXp-0dv;AXp-1d;AXp-1dv;AXp-2d;AXp-2dv;AXp-3d;AXp-3dv;AXp-4d;AXp-4dv;AXp-5d;AXp-5dv;AXp-6d;AXp-6dv;AXp-7d;AXp-7dv;BCUTZ-1h;BCUTZ-1l;BCUTare-1h;BCUTare-1l;BCUTc-1h;BCUTc-1l;BCUTd-1h;BCUTd-1l;BCUTdv-1h;BCUTdv-1l;BCUTi-1h;BCUTi-1l;BCUTm-1h;BCUTm-1l;BCUTp-1h;BCUTp-1l;BCUTpe-1h;BCUTpe-1l;BCUTs-1h;BCUTs-1l;BCUTse-1h;BCUTse-1l;BCUTv-1h;BCUTv-1l;BIC0;BIC1;BIC2;BIC3;BIC4;BIC5;BalabanJ;BertzCT;C1SP1;C1SP2;C1SP3;C2SP1;C2SP2;C2SP3;C3SP2;C3SP3;C4SP3;CIC0;CIC1;CIC2;CIC3;CIC4;CIC5;DPSA1;DPSA2;DPSA3;DPSA4;DPSA5;DetourIndex;Diameter;ECIndex;EState_VSA1;EState_VSA10;EState_VSA2;EState_VSA3;EState_VSA4;EState_VSA5;EState_VSA6;EState_VSA7;EState_VSA8;EState_VSA9;ETA_alpha;ETA_beta;ETA_beta_ns;ETA_beta_ns_d;ETA_beta_s;ETA_dAlpha_A;ETA_dAlpha_B;ETA_dBeta;ETA_dEpsilon_A;ETA_dEpsilon_B;ETA_dEpsilon_C;ETA_dEpsilon_D;ETA_dPsi_A;ETA_dPsi_B;ETA_epsilon_1;ETA_epsilon_2;ETA_epsilon_3;ETA_epsilon_4;ETA_epsilon_5;ETA_eta;ETA_eta_B;ETA_eta_BR;ETA_eta_F;ETA_eta_FL;ETA_eta_L;ETA_eta_R;ETA_eta_RL;ETA_psi_1;ETA_shape_p;ETA_shape_x;ETA_shape_y;FCSP3;FNSA1;FNSA2;FNSA3;FNSA4;FNSA5;FPSA1;FPSA2;FPSA3;FPSA4;FPSA5;FilterItLogS;GATS1Z;GATS1are;GATS1c;GATS1d;GATS1dv;GATS1i;GATS1m;GATS1p;GATS1pe;GATS1s;GATS1se;GATS1v;GATS2Z;GATS2are;GATS2c;GATS2d;GATS2dv;GATS2i;GATS2m;GATS2p;GATS2pe;GATS2s;GATS2se;GATS2v;GATS3Z;GATS3are;GATS3c;GATS3d;GATS3dv;GATS3i;GATS3m;GATS3p;GATS3pe;GATS3s;GATS3se;GATS3v;GATS4Z;GATS4are;GATS4c;GATS4d;GATS4dv;GATS4i;GATS4m;GATS4p;GATS4pe;GATS4s;GATS4se;GATS4v;GATS5Z;GATS5are;GATS5c;GATS5d;GATS5dv;GATS5i;GATS5m;GATS5p;GATS5pe;GATS5s;GATS5se;GATS5v;GATS6Z;GATS6are;GATS6c;GATS6d;GATS6dv;GATS6i;GATS6m;GATS6p;GATS6pe;GATS6s;GATS6se;GATS6v;GATS7Z;GATS7are;GATS7c;GATS7d;GATS7dv;GATS7i;GATS7m;GATS7p;GATS7pe;GATS7s;GATS7se;GATS7v;GATS8Z;GATS8are;GATS8c;GATS8d;GATS8dv;GATS8i;GATS8m;GATS8p;GATS8pe;GATS8s;GATS8se;GATS8v;GGI1;GGI10;GGI2;GGI3;GGI4;GGI5;GGI6;GGI7;GGI8;GGI9;GRAV;GRAVH;GRAVHp;GRAVp;GeomDiameter;GeomPetitjeanIndex;GeomRadius;GeomShapeIndex;GhoseFilter;HybRatio;IC0;IC1;IC2;IC3;IC4;IC5;JGI1;JGI10;JGI2;JGI3;JGI4;JGI5;JGI6;JGI7;JGI8;JGI9;JGT10;Kier1;Kier2;Kier3;LabuteASA;Lipinski;LogEE_A;LogEE_D;LogEE_Dt;LogEE_DzZ;LogEE_Dzare;LogEE_Dzi;LogEE_Dzm;LogEE_Dzp;LogEE_Dzpe;LogEE_Dzse;LogEE_Dzv;MATS1Z;MATS1are;MATS1c;MATS1d;MATS1dv;MATS1i;MATS1m;MATS1p;MATS1pe;MATS1s;MATS1se;MATS1v;MATS2Z;MATS2are;MATS2c;MATS2d;MATS2dv;MATS2i;MATS2m;MATS2p;MATS2pe;MATS2s;MATS2se;MATS2v;MATS3Z;MATS3are;MATS3c;MATS3d;MATS3dv;MATS3i;MATS3m;MATS3p;MATS3pe;MATS3s;MATS3se;MATS3v;MATS4Z;MATS4are;MATS4c;MATS4d;MATS4dv;MATS4i;MATS4m;MATS4p;MATS4pe;MATS4s;MATS4se;MATS4v;MATS5Z;MATS5are;MATS5c;MATS5d;MATS5dv;MATS5i;MATS5m;MATS5p;MATS5pe;MATS5s;MATS5se;MATS5v;MATS6Z;MATS6are;MATS6c;MATS6d;MATS6dv;MATS6i;MATS6m;MATS6p;MATS6pe;MATS6s;MATS6se;MATS6v;MATS7Z;MATS7are;MATS7c;MATS7d;MATS7dv;MATS7i;MATS7m;MATS7p;MATS7pe;MATS7s;MATS7se;MATS7v;MATS8Z;MATS8are;MATS8c;MATS8d;MATS8dv;MATS8i;MATS8m;MATS8p;MATS8pe;MATS8s;MATS8se;MATS8v;MDEC-22;MDEC-23;MDEC-33;MIC0;MIC1;MIC2;MIC3;MIC4;MIC5;MID;MID_C;MID_N;MID_O;MID_X;MID_h;MOMI-X;MOMI-Y;MOMI-Z;MPC10;MPC2;MPC3;MPC4;MPC5;MPC6;MPC7;MPC8;MPC9;MW;MWC01;MWC02;MWC03;MWC04;MWC05;MWC06;MWC07;MWC08;MWC09;MWC10;MZ;Mare;Mi;Mm;Mor01;Mor01m;Mor01p;Mor01se;Mor01v;Mor02;Mor02m;Mor02p;Mor02se;Mor02v;Mor03;Mor03m;Mor03p;Mor03se;Mor03v;Mor04;Mor04m;Mor04p;Mor04se;Mor04v;Mor05;Mor05m;Mor05p;Mor05se;Mor05v;Mor06;Mor06m;Mor06p;Mor06se;Mor06v;Mor07;Mor07m;Mor07p;Mor07se;Mor07v;Mor08;Mor08m;Mor08p;Mor08se;Mor08v;Mor09;Mor09m;Mor09p;Mor09se;Mor09v;Mor10;Mor10m;Mor10p;Mor10se;Mor10v;Mor11;Mor11m;Mor11p;Mor11se;Mor11v;Mor12;Mor12m;Mor12p;Mor12se;Mor12v;Mor13;Mor13m;Mor13p;Mor13se;Mor13v;Mor14;Mor14m;Mor14p;Mor14se;Mor14v;Mor15;Mor15m;Mor15p;Mor15se;Mor15v;Mor16;Mor16m;Mor16p;Mor16se;Mor16v;Mor17;Mor17m;Mor17p;Mor17se;Mor17v;Mor18;Mor18m;Mor18p;Mor18se;Mor18v;Mor19;Mor19m;Mor19p;Mor19se;Mor19v;Mor20;Mor20m;Mor20p;Mor20se;Mor20v;Mor21;Mor21m;Mor21p;Mor21se;Mor21v;Mor22;Mor22m;Mor22p;Mor22se;Mor22v;Mor23;Mor23m;Mor23p;Mor23se;Mor23v;Mor24;Mor24m;Mor24p;Mor24se;Mor24v;Mor25;Mor25m;Mor25p;Mor25se;Mor25v;Mor26;Mor26m;Mor26p;Mor26se;Mor26v;Mor27;Mor27m;Mor27p;Mor27se;Mor27v;Mor28;Mor28m;Mor28p;Mor28se;Mor28v;Mor29;Mor29m;Mor29p;Mor29se;Mor29v;Mor30;Mor30m;Mor30p;Mor30se;Mor30v;Mor31;Mor31m;Mor31p;Mor31se;Mor31v;Mor32;Mor32m;Mor32p;Mor32se;Mor32v;Mp;Mpe;Mse;Mv;NaaCH;NaaN;NaaNH;NaaO;NaaS;NaaaC;NaasC;NaasN;NdCH2;NdO;NdS;NddsN;NddssS;NdsCH;NdsN;NdssC;NsBr;NsCH3;NsCl;NsF;NsNH2;NsOH;NssCH2;NssNH;NssO;NssS;NsssCH;NsssN;NssssC;NtN;NtsC;PEOE_VSA1;PEOE_VSA10;PEOE_VSA11;PEOE_VSA12;PEOE_VSA13;PEOE_VSA2;PEOE_VSA3;PEOE_VSA4;PEOE_VSA5;PEOE_VSA6;PEOE_VSA7;PEOE_VSA8;PEOE_VSA9;PNSA1;PNSA2;PNSA3;PNSA4;PNSA5;PPSA1;PPSA2;PPSA3;PPSA4;PPSA5;PetitjeanIndex;RASA;RNCG;RNCS;RPCG;RPCS;RPSA;Radius;RotRatio;SIC0;SIC1;SIC2;SIC3;SIC4;SIC5;SLogP;SM1_Dt;SM1_DzZ;SM1_Dzare;SM1_Dzi;SM1_Dzm;SM1_Dzp;SM1_Dzpe;SM1_Dzse;SM1_Dzv;SMR;SMR_VSA1;SMR_VSA2;SMR_VSA3;SMR_VSA4;SMR_VSA5;SMR_VSA6;SMR_VSA7;SMR_VSA8;SMR_VSA9;SRW02;SRW03;SRW04;SRW05;SRW06;SRW07;SRW08;SRW09;SRW10;SZ;SaaCH;SaaN;SaaNH;SaaO;SaaS;SaaaC;SaasC;SaasN;Sare;SdCH2;SdO;SdS;SddsN;SddssS;SdsCH;SdsN;SdssC;Si;SlogP_VSA1;SlogP_VSA10;SlogP_VSA11;SlogP_VSA2;SlogP_VSA3;SlogP_VSA4;SlogP_VSA5;SlogP_VSA6;SlogP_VSA7;SlogP_VSA8;SlogP_VSA9;Sm;Sp;SpAD_A;SpAD_D;SpAD_Dt;SpAD_DzZ;SpAD_Dzare;SpAD_Dzi;SpAD_Dzm;SpAD_Dzp;SpAD_Dzpe;SpAD_Dzse;SpAD_Dzv;SpAbs_A;SpAbs_D;SpAbs_Dt;SpAbs_DzZ;SpAbs_Dzare;SpAbs_Dzi;SpAbs_Dzm;SpAbs_Dzp;SpAbs_Dzpe;SpAbs_Dzse;SpAbs_Dzv;SpDiam_A;SpDiam_D;SpDiam_Dt;SpDiam_DzZ;SpDiam_Dzare;SpDiam_Dzi;SpDiam_Dzm;SpDiam_Dzp;SpDiam_Dzpe;SpDiam_Dzse;SpDiam_Dzv;SpMAD_A;SpMAD_D;SpMAD_Dt;SpMAD_DzZ;SpMAD_Dzare;SpMAD_Dzi;SpMAD_Dzm;SpMAD_Dzp;SpMAD_Dzpe;SpMAD_Dzse;SpMAD_Dzv;SpMax_A;SpMax_D;SpMax_Dt;SpMax_DzZ;SpMax_Dzare;SpMax_Dzi;SpMax_Dzm;SpMax_Dzp;SpMax_Dzpe;SpMax_Dzse;SpMax_Dzv;Spe;SsBr;SsCH3;SsCl;SsF;SsNH2;SsOH;Sse;SssCH2;SssNH;SssO;SssS;SsssCH;SsssN;SssssC;StN;StsC;Sv;TASA;TIC0;TIC1;TIC2;TIC3;TIC4;TIC5;TMPC10;TMWC10;TPSA;TSRW10;TopoPSA;TopoPSA(NO);TopoShapeIndex;TpiPC10;VAdjMat;VE1_A;VE1_D;VE1_Dt;VE1_DzZ;VE1_Dzare;VE1_Dzi;VE1_Dzm;VE1_Dzp;VE1_Dzpe;VE1_Dzse;VE1_Dzv;VE2_A;VE2_D;VE2_Dt;VE2_DzZ;VE2_Dzare;VE2_Dzi;VE2_Dzm;VE2_Dzp;VE2_Dzpe;VE2_Dzse;VE2_Dzv;VE3_A;VE3_D;VE3_Dt;VE3_DzZ;VE3_Dzare;VE3_Dzi;VE3_Dzm;VE3_Dzp;VE3_Dzpe;VE3_Dzse;VE3_Dzv;VMcGowan;VR1_A;VR1_D;VR1_Dt;VR1_DzZ;VR1_Dzare;VR1_Dzi;VR1_Dzm;VR1_Dzp;VR1_Dzpe;VR1_Dzse;VR1_Dzv;VR2_A;VR2_D;VR2_Dt;VR2_DzZ;VR2_Dzare;VR2_Dzi;VR2_Dzm;VR2_Dzp;VR2_Dzpe;VR2_Dzse;VR2_Dzv;VR3_A;VR3_D;VR3_Dt;VR3_DzZ;VR3_Dzare;VR3_Dzi;VR3_Dzm;VR3_Dzp;VR3_Dzpe;VR3_Dzse;VR3_Dzv;VSA_EState1;VSA_EState2;VSA_EState3;VSA_EState4;VSA_EState5;VSA_EState6;VSA_EState7;VSA_EState8;VSA_EState9;Vabc;WNSA1;WNSA2;WNSA3;WNSA4;WNSA5;WPSA1;WPSA2;WPSA3;WPSA4;WPSA5;WPath;WPol;Xc-3d;Xc-3dv;Xc-4d;Xc-4dv;Xc-5d;Xc-5dv;Xc-6d;Xc-6dv;Xch-3d;Xch-3dv;Xch-4d;Xch-4dv;Xch-5d;Xch-5dv;Xch-6d;Xch-6dv;Xch-7d;Xch-7dv;Xp-0d;Xp-0dv;Xp-1d;Xp-1dv;Xp-2d;Xp-2dv;Xp-3d;Xp-3dv;Xp-4d;Xp-4dv;Xp-5d;Xp-5dv;Xp-6d;Xp-6dv;Xp-7d;Xp-7dv;Xpc-4d;Xpc-4dv;Xpc-5d;Xpc-5dv;Xpc-6d;Xpc-6dv;ZMIC0;ZMIC1;ZMIC2;ZMIC3;ZMIC4;ZMIC5;Zagreb1;Zagreb2;apol;bpol;fMF;fragCpx;mZagreb1;mZagreb2;n10AHRing;n10ARing;n10FAHRing;n10FARing;n10FHRing;n10FRing;n10FaHRing;n10FaRing;n10HRing;n10Ring;n10aHRing;n10aRing;n11AHRing;n11ARing;n11FAHRing;n11FARing;n11FHRing;n11FRing;n11FaHRing;n11FaRing;n11HRing;n11Ring;n11aHRing;n11aRing;n12AHRing;n12ARing;n12FAHRing;n12FARing;n12FHRing;n12FRing;n12FaHRing;n12FaRing;n12HRing;n12Ring;n12aHRing;n12aRing;n3AHRing;n3ARing;n3HRing;n3Ring;n3aHRing;n3aRing;n4AHRing;n4ARing;n4FAHRing;n4FARing;n4FHRing;n4FRing;n4FaHRing;n4FaRing;n4HRing;n4Ring;n4aHRing;n4aRing;n5AHRing;n5ARing;n5FAHRing;n5FARing;n5FHRing;n5FRing;n5FaHRing;n5FaRing;n5HRing;n5Ring;n5aHRing;n5aRing;n6AHRing;n6ARing;n6FAHRing;n6FARing;n6FHRing;n6FRing;n6FaHRing;n6FaRing;n6HRing;n6Ring;n6aHRing;n6aRing;n7AHRing;n7ARing;n7FAHRing;n7FARing;n7FHRing;n7FRing;n7FaHRing;n7FaRing;n7HRing;n7Ring;n7aHRing;n7aRing;n8AHRing;n8ARing;n8FAHRing;n8FARing;n8FHRing;n8FRing;n8FaHRing;n8FaRing;n8HRing;n8Ring;n8aHRing;n8aRing;n9AHRing;n9ARing;n9FAHRing;n9FARing;n9FHRing;n9FRing;n9FaHRing;n9FaRing;n9HRing;n9Ring;n9aHRing;n9aRing;nAHRing;nARing;nAcid;nAromAtom;nAromBond;nAtom;nB;nBase;nBonds;nBondsA;nBondsD;nBondsKD;nBondsKS;nBondsM;nBondsO;nBondsS;nBondsT;nBr;nBridgehead;nC;nCl;nF;nFAHRing;nFARing;nFHRing;nFRing;nFaHRing;nFaRing;nG12AHRing;nG12ARing;nG12FAHRing;nG12FARing;nG12FHRing;nG12FRing;nG12FaHRing;nG12FaRing;nG12HRing;nG12Ring;nG12aHRing;nG12aRing;nH;nHBAcc;nHBDon;nHRing;nHeavyAtom;nHetero;nI;nN;nO;nP;nRing;nRot;nS;nSpiro;nX;naHRing;naRing;piPC1;piPC10;piPC2;piPC3;piPC4;piPC5;piPC6;piPC7;piPC8;piPC9,0,mordred rdkit_raw,MaxEStateIndex;MinEStateIndex;MaxAbsEStateIndex;MinAbsEStateIndex;qed;MolWt;HeavyAtomMolWt;ExactMolWt;NumValenceElectrons;NumRadicalElectrons;MaxPartialCharge;MinPartialCharge;MaxAbsPartialCharge;MinAbsPartialCharge;FpDensityMorgan1;FpDensityMorgan2;FpDensityMorgan3;BalabanJ;BertzCT;Chi0;Chi0n;Chi0v;Chi1;Chi1n;Chi1v;Chi2n;Chi2v;Chi3n;Chi3v;Chi4n;Chi4v;HallKierAlpha;Ipc;Kappa1;Kappa2;Kappa3;LabuteASA;PEOE_VSA1;PEOE_VSA10;PEOE_VSA11;PEOE_VSA12;PEOE_VSA13;PEOE_VSA14;PEOE_VSA2;PEOE_VSA3;PEOE_VSA4;PEOE_VSA5;PEOE_VSA6;PEOE_VSA7;PEOE_VSA8;PEOE_VSA9;SMR_VSA1;SMR_VSA10;SMR_VSA2;SMR_VSA3;SMR_VSA4;SMR_VSA5;SMR_VSA6;SMR_VSA7;SMR_VSA8;SMR_VSA9;SlogP_VSA1;SlogP_VSA10;SlogP_VSA11;SlogP_VSA12;SlogP_VSA2;SlogP_VSA3;SlogP_VSA4;SlogP_VSA5;SlogP_VSA6;SlogP_VSA7;SlogP_VSA8;SlogP_VSA9;TPSA;EState_VSA1;EState_VSA10;EState_VSA11;EState_VSA2;EState_VSA3;EState_VSA4;EState_VSA5;EState_VSA6;EState_VSA7;EState_VSA8;EState_VSA9;VSA_EState1;VSA_EState10;VSA_EState2;VSA_EState3;VSA_EState4;VSA_EState5;VSA_EState6;VSA_EState7;VSA_EState8;VSA_EState9;FractionCSP3;HeavyAtomCount;NHOHCount;NOCount;NumAliphaticCarbocycles;NumAliphaticHeterocycles;NumAliphaticRings;NumAromaticCarbocycles;NumAromaticHeterocycles;NumAromaticRings;NumHAcceptors;NumHDonors;NumHeteroatoms;NumRotatableBonds;NumSaturatedCarbocycles;NumSaturatedHeterocycles;NumSaturatedRings;RingCount;MolLogP;MolMR;fr_Al_COO;fr_Al_OH;fr_Al_OH_noTert;fr_ArN;fr_Ar_COO;fr_Ar_N;fr_Ar_NH;fr_Ar_OH;fr_COO;fr_COO2;fr_C_O;fr_C_O_noCOO;fr_C_S;fr_HOCCN;fr_Imine;fr_NH0;fr_NH1;fr_NH2;fr_N_O;fr_Ndealkylation1;fr_Ndealkylation2;fr_Nhpyrrole;fr_SH;fr_aldehyde;fr_alkyl_carbamate;fr_alkyl_halide;fr_allylic_oxid;fr_amide;fr_amidine;fr_aniline;fr_aryl_methyl;fr_azide;fr_azo;fr_barbitur;fr_benzene;fr_benzodiazepine;fr_bicyclic;fr_diazo;fr_dihydropyridine;fr_epoxide;fr_ester;fr_ether;fr_furan;fr_guanido;fr_halogen;fr_hdrzine;fr_hdrzone;fr_imidazole;fr_imide;fr_isocyan;fr_isothiocyan;fr_ketone;fr_ketone_Topliss;fr_lactam;fr_lactone;fr_methoxy;fr_morpholine;fr_nitrile;fr_nitro;fr_nitro_arom;fr_nitro_arom_nonortho;fr_nitroso;fr_oxazole;fr_oxime;fr_para_hydroxylation;fr_phenol;fr_phenol_noOrthoHbond;fr_phos_acid;fr_phos_ester;fr_piperdine;fr_piperzine;fr_priamide;fr_prisulfonamd;fr_pyridine;fr_quatN;fr_sulfide;fr_sulfonamd;fr_sulfone;fr_term_acetylene;fr_tetrazole;fr_thiazole;fr_thiocyan;fr_thiophene;fr_unbrch_alkane;fr_urea,0,rdkit +rdkit_scaled,MaxEStateIndex_per_heavyatom;MinEStateIndex_per_heavyatom;MaxAbsEStateIndex_per_heavyatom;MinAbsEStateIndex;qed_per_heavyatom;MolWt_per_heavyatom;HeavyAtomMolWt_per_heavyatom;ExactMolWt_per_heavyatom;NumValenceElectrons;NumRadicalElectrons;MaxPartialCharge;MinPartialCharge;MaxAbsPartialCharge;MinAbsPartialCharge;FpDensityMorgan1_per_heavyatom;FpDensityMorgan2_per_heavyatom;FpDensityMorgan3_per_heavyatom;BalabanJ;BertzCT_per_heavyatom;Chi0_per_heavyatom;Chi0n_per_heavyatom;Chi0v_per_heavyatom;Chi1_per_heavyatom;Chi1n_per_heavyatom;Chi1v_per_heavyatom;Chi2n_per_heavyatom;Chi2v_per_heavyatom;Chi3n_per_heavyatom;Chi3v_per_heavyatom;Chi4n_per_heavyatom;Chi4v_per_heavyatom;HallKierAlpha_per_heavyatom;AvgIpc;Kappa1_per_heavyatom;Kappa2_per_heavyatom;Kappa3;LabuteASA_per_heavyatom;PEOE_VSA1_per_heavyatom;PEOE_VSA10_per_heavyatom;PEOE_VSA11_per_heavyatom;PEOE_VSA12_per_heavyatom;PEOE_VSA13;PEOE_VSA14;PEOE_VSA2_per_heavyatom;PEOE_VSA3;PEOE_VSA4;PEOE_VSA5;PEOE_VSA6_per_heavyatom;PEOE_VSA7_per_heavyatom;PEOE_VSA8_per_heavyatom;PEOE_VSA9_per_heavyatom;SMR_VSA1_per_heavyatom;SMR_VSA10_per_heavyatom;SMR_VSA2;SMR_VSA3_per_heavyatom;SMR_VSA4;SMR_VSA5_per_heavyatom;SMR_VSA6;SMR_VSA7;SMR_VSA8;SMR_VSA9;SlogP_VSA1_per_heavyatom;SlogP_VSA10;SlogP_VSA11;SlogP_VSA12;SlogP_VSA2_per_heavyatom;SlogP_VSA3_per_heavyatom;SlogP_VSA4;SlogP_VSA5_per_heavyatom;SlogP_VSA6;SlogP_VSA7;SlogP_VSA8;SlogP_VSA9;TPSA_per_heavyatom;EState_VSA1_per_heavyatom;EState_VSA10_per_heavyatom;EState_VSA11_per_heavyatom;EState_VSA2;EState_VSA3;EState_VSA4;EState_VSA5;EState_VSA6;EState_VSA7;EState_VSA8;EState_VSA9_per_heavyatom;VSA_EState1_per_heavyatom;VSA_EState10_per_heavyatom;VSA_EState2_per_heavyatom;VSA_EState3_per_heavyatom;VSA_EState4_per_heavyatom;VSA_EState5_per_heavyatom;VSA_EState6_per_heavyatom;VSA_EState7_per_heavyatom;VSA_EState8_per_heavyatom;VSA_EState9_per_heavyatom;FractionCSP3;HeavyAtomCount;NHOHCount;NOCount;NumAliphaticCarbocycles;NumAliphaticHeterocycles;NumAliphaticRings;NumAromaticCarbocycles;NumAromaticHeterocycles;NumAromaticRings;NumHAcceptors;NumHDonors;NumHeteroatoms;NumRotatableBonds;NumSaturatedCarbocycles;NumSaturatedHeterocycles;NumSaturatedRings;RingCount;MolLogP;MolMR_per_heavyatom;fr_Al_COO;fr_Al_OH;fr_Al_OH_noTert;fr_ArN;fr_Ar_COO;fr_Ar_N;fr_Ar_NH;fr_Ar_OH;fr_COO;fr_COO2;fr_C_O;fr_C_O_noCOO;fr_C_S;fr_HOCCN;fr_Imine;fr_NH0;fr_NH1;fr_NH2;fr_N_O;fr_Ndealkylation1;fr_Ndealkylation2;fr_Nhpyrrole;fr_SH;fr_aldehyde;fr_alkyl_carbamate;fr_alkyl_halide;fr_allylic_oxid;fr_amide;fr_amidine;fr_aniline;fr_aryl_methyl;fr_azide;fr_azo;fr_barbitur;fr_benzene;fr_benzodiazepine;fr_bicyclic;fr_diazo;fr_dihydropyridine;fr_epoxide;fr_ester;fr_ether;fr_furan;fr_guanido;fr_halogen;fr_hdrzine;fr_hdrzone;fr_imidazole;fr_imide;fr_isocyan;fr_isothiocyan;fr_ketone;fr_ketone_Topliss;fr_lactam;fr_lactone;fr_methoxy;fr_morpholine;fr_nitrile;fr_nitro;fr_nitro_arom;fr_nitro_arom_nonortho;fr_nitroso;fr_oxazole;fr_oxime;fr_para_hydroxylation;fr_phenol;fr_phenol_noOrthoHbond;fr_phos_acid;fr_phos_ester;fr_piperdine;fr_piperzine;fr_priamide;fr_prisulfonamd;fr_pyridine;fr_quatN;fr_sulfide;fr_sulfonamd;fr_sulfone;fr_term_acetylene;fr_tetrazole;fr_thiazole;fr_thiocyan;fr_thiophene;fr_unbrch_alkane;fr_urea,1,rdkit +mordred_filtered_scaled,AATS0Z;AATS0are;AATS0d;AATS0dv;AATS0i;AATS0m;AATS0p;AATS0pe;AATS0s;AATS0se;AATS0v;AATS1Z;AATS1are;AATS1d;AATS1dv;AATS1i;AATS1m;AATS1p;AATS1pe;AATS1s;AATS1se;AATS1v;AATS2Z;AATS2are;AATS2d;AATS2dv;AATS2i;AATS2m;AATS2p;AATS2pe;AATS2s;AATS2se;AATS2v;AATS3Z;AATS3are;AATS3d;AATS3dv;AATS3i;AATS3m;AATS3p;AATS3pe;AATS3s;AATS3se;AATS3v;AATS4Z;AATS4are;AATS4d;AATS4dv;AATS4i;AATS4m;AATS4p;AATS4pe;AATS4s;AATS4se;AATS4v;AATS5Z;AATS5are;AATS5d;AATS5dv;AATS5i;AATS5m;AATS5p;AATS5pe;AATS5s;AATS5se;AATS5v;AATS6Z;AATS6are;AATS6d;AATS6dv;AATS6i;AATS6m;AATS6p;AATS6pe;AATS6s;AATS6se;AATS6v;AATS7Z;AATS7are;AATS7d;AATS7dv;AATS7i;AATS7m;AATS7p;AATS7pe;AATS7s;AATS7se;AATS7v;AATS8Z;AATS8are;AATS8d;AATS8dv;AATS8i;AATS8m;AATS8p;AATS8pe;AATS8s;AATS8se;AATS8v;AATSC0Z;AATSC0are;AATSC0c;AATSC0d;AATSC0dv;AATSC0i;AATSC0m;AATSC0p;AATSC0pe;AATSC0s;AATSC0se;AATSC0v;AATSC1Z;AATSC1are;AATSC1c;AATSC1d;AATSC1dv;AATSC1i;AATSC1m;AATSC1p;AATSC1pe;AATSC1s;AATSC1se;AATSC1v;AATSC2Z;AATSC2are;AATSC2c;AATSC2d;AATSC2dv;AATSC2i;AATSC2m;AATSC2p;AATSC2pe;AATSC2s;AATSC2se;AATSC2v;AATSC3Z;AATSC3are;AATSC3c;AATSC3d;AATSC3dv;AATSC3i;AATSC3m;AATSC3p;AATSC3pe;AATSC3s;AATSC3se;AATSC3v;AATSC4Z;AATSC4are;AATSC4c;AATSC4d;AATSC4dv;AATSC4i;AATSC4m;AATSC4p;AATSC4pe;AATSC4s;AATSC4se;AATSC4v;AATSC5Z;AATSC5are;AATSC5c;AATSC5d;AATSC5dv;AATSC5i;AATSC5m;AATSC5p;AATSC5pe;AATSC5s;AATSC5se;AATSC5v;AATSC6Z;AATSC6are;AATSC6c;AATSC6d;AATSC6dv;AATSC6i;AATSC6m;AATSC6p;AATSC6pe;AATSC6s;AATSC6se;AATSC6v;AATSC7Z;AATSC7are;AATSC7c;AATSC7d;AATSC7dv;AATSC7i;AATSC7m;AATSC7p;AATSC7pe;AATSC7s;AATSC7se;AATSC7v;AATSC8Z;AATSC8are;AATSC8c;AATSC8d;AATSC8dv;AATSC8i;AATSC8m;AATSC8p;AATSC8pe;AATSC8s;AATSC8se;AATSC8v;ABC;ABCGG;AETA_alpha;AETA_beta;AETA_beta_ns;AETA_beta_ns_d;AETA_beta_s;AETA_dBeta;AETA_eta_per_heavyatom;AETA_eta_B;AETA_eta_BR;AETA_eta_F_per_heavyatom;AETA_eta_FL;AETA_eta_L;AETA_eta_R_per_heavyatom;AETA_eta_RL;AMID;AMID_C;AMID_N;AMID_O;AMID_X;AMID_h;AMW;ATS0Z;ATS0are_per_heavyatom;ATS0d;ATS0dv_per_heavyatom;ATS0i_per_heavyatom;ATS0m_per_heavyatom;ATS0p_per_heavyatom;ATS0pe_per_heavyatom;ATS0s_per_heavyatom;ATS0se_per_heavyatom;ATS0v_per_heavyatom;ATS1Z;ATS1are_per_heavyatom;ATS1d;ATS1dv_per_heavyatom;ATS1i_per_heavyatom;ATS1m_per_heavyatom;ATS1p_per_heavyatom;ATS1pe_per_heavyatom;ATS1s_per_heavyatom;ATS1se_per_heavyatom;ATS1v_per_heavyatom;ATS2Z;ATS2are_per_heavyatom;ATS2d;ATS2dv_per_heavyatom;ATS2i_per_heavyatom;ATS2m_per_heavyatom;ATS2p_per_heavyatom;ATS2pe_per_heavyatom;ATS2s_per_heavyatom;ATS2se_per_heavyatom;ATS2v_per_heavyatom;ATS3Z;ATS3are_per_heavyatom;ATS3d;ATS3dv_per_heavyatom;ATS3i_per_heavyatom;ATS3m_per_heavyatom;ATS3p_per_heavyatom;ATS3pe_per_heavyatom;ATS3s_per_heavyatom;ATS3se_per_heavyatom;ATS3v_per_heavyatom;ATS4Z;ATS4are_per_heavyatom;ATS4d;ATS4dv_per_heavyatom;ATS4i_per_heavyatom;ATS4m_per_heavyatom;ATS4p_per_heavyatom;ATS4pe_per_heavyatom;ATS4s_per_heavyatom;ATS4se_per_heavyatom;ATS4v_per_heavyatom;ATS5Z;ATS5are_per_heavyatom;ATS5d;ATS5dv_per_heavyatom;ATS5i_per_heavyatom;ATS5m_per_heavyatom;ATS5p_per_heavyatom;ATS5pe_per_heavyatom;ATS5s_per_heavyatom;ATS5se_per_heavyatom;ATS5v_per_heavyatom;ATS6Z;ATS6are_per_heavyatom;ATS6d;ATS6dv_per_heavyatom;ATS6i_per_heavyatom;ATS6m_per_heavyatom;ATS6p_per_heavyatom;ATS6pe_per_heavyatom;ATS6s_per_heavyatom;ATS6se_per_heavyatom;ATS6v_per_heavyatom;ATS7Z;ATS7are_per_heavyatom;ATS7d;ATS7dv_per_heavyatom;ATS7i_per_heavyatom;ATS7m_per_heavyatom;ATS7p_per_heavyatom;ATS7pe_per_heavyatom;ATS7s_per_heavyatom;ATS7se_per_heavyatom;ATS7v_per_heavyatom;ATS8Z;ATS8are_per_heavyatom;ATS8d;ATS8dv_per_heavyatom;ATS8i_per_heavyatom;ATS8m_per_heavyatom;ATS8p_per_heavyatom;ATS8pe_per_heavyatom;ATS8s_per_heavyatom;ATS8se_per_heavyatom;ATS8v_per_heavyatom;ATSC0Z;ATSC0are_per_heavyatom;ATSC0c_per_heavyatom;ATSC0d_per_heavyatom;ATSC0dv_per_heavyatom;ATSC0i_per_heavyatom;ATSC0m;ATSC0p_per_heavyatom;ATSC0pe_per_heavyatom;ATSC0s_per_heavyatom;ATSC0se_per_heavyatom;ATSC0v_per_heavyatom;ATSC1Z;ATSC1are;ATSC1c_per_heavyatom;ATSC1d_per_heavyatom;ATSC1dv_per_heavyatom;ATSC1i;ATSC1m;ATSC1p;ATSC1pe;ATSC1s;ATSC1se;ATSC1v;ATSC2Z;ATSC2are;ATSC2c;ATSC2d;ATSC2dv;ATSC2i;ATSC2m;ATSC2p;ATSC2pe;ATSC2s;ATSC2se;ATSC2v;ATSC3Z;ATSC3are;ATSC3c_per_heavyatom;ATSC3d;ATSC3dv;ATSC3i;ATSC3m;ATSC3p;ATSC3pe;ATSC3s;ATSC3se;ATSC3v;ATSC4Z;ATSC4are;ATSC4c_per_heavyatom;ATSC4d;ATSC4dv;ATSC4i;ATSC4m;ATSC4p;ATSC4pe;ATSC4s;ATSC4se;ATSC4v;ATSC5Z;ATSC5are;ATSC5c;ATSC5d;ATSC5dv;ATSC5i;ATSC5m;ATSC5p;ATSC5pe;ATSC5s;ATSC5se;ATSC5v;ATSC6Z;ATSC6are;ATSC6c;ATSC6d;ATSC6dv;ATSC6i;ATSC6m;ATSC6p;ATSC6pe;ATSC6s;ATSC6se;ATSC6v;ATSC7Z;ATSC7are;ATSC7c_per_heavyatom;ATSC7d;ATSC7dv;ATSC7i;ATSC7m;ATSC7p;ATSC7pe;ATSC7s;ATSC7se;ATSC7v;ATSC8Z;ATSC8are;ATSC8c;ATSC8d;ATSC8dv;ATSC8i;ATSC8m;ATSC8p;ATSC8pe;ATSC8s;ATSC8se;ATSC8v;AXp-0d;AXp-0dv;AXp-1d;AXp-1dv;AXp-2d;AXp-2dv;AXp-3d;AXp-3dv;AXp-4d;AXp-4dv;AXp-5d;AXp-5dv;AXp-6d;AXp-6dv;AXp-7d;AXp-7dv;BCUTZ-1h;BCUTZ-1l;BCUTare-1h;BCUTare-1l;BCUTc-1h;BCUTc-1l;BCUTd-1h;BCUTd-1l;BCUTdv-1h;BCUTdv-1l;BCUTi-1h;BCUTi-1l;BCUTm-1h;BCUTm-1l;BCUTp-1h;BCUTp-1l;BCUTpe-1h;BCUTpe-1l;BCUTs-1h;BCUTs-1l;BCUTse-1h;BCUTse-1l;BCUTv-1h;BCUTv-1l;BIC0_per_heavyatom;BIC1_per_heavyatom;BIC2;BIC3;BIC4;BIC5;BalabanJ;BertzCT_per_heavyatom;C1SP1;C1SP2;C1SP3;C2SP1;C2SP2;C2SP3;C3SP2;C3SP3;C4SP3;CIC0_per_heavyatom;CIC1_per_heavyatom;CIC2_per_heavyatom;CIC3_per_heavyatom;CIC4;CIC5;DPSA1_per_heavyatom;DPSA2_per_heavyatom;DPSA3_per_heavyatom;DPSA4_per_heavyatom;DPSA5_per_heavyatom;DetourIndex;Diameter;ECIndex;EState_VSA1_per_heavyatom;EState_VSA10_per_heavyatom;EState_VSA2_per_heavyatom;EState_VSA3;EState_VSA4;EState_VSA5;EState_VSA6;EState_VSA7;EState_VSA8;EState_VSA9;ETA_alpha_per_heavyatom;ETA_beta_per_heavyatom;ETA_beta_ns_per_heavyatom;ETA_beta_ns_d;ETA_beta_s_per_heavyatom;ETA_dAlpha_A;ETA_dAlpha_B;ETA_dBeta_per_heavyatom;ETA_dEpsilon_A;ETA_dEpsilon_B;ETA_dEpsilon_C;ETA_dEpsilon_D;ETA_dPsi_A;ETA_dPsi_B;ETA_epsilon_1;ETA_epsilon_2;ETA_epsilon_3;ETA_epsilon_4;ETA_epsilon_5;ETA_eta_per_heavyatom;ETA_eta_B_per_heavyatom;ETA_eta_BR_per_heavyatom;ETA_eta_F_per_heavyatom;ETA_eta_FL_per_heavyatom;ETA_eta_L_per_heavyatom;ETA_eta_R_per_heavyatom;ETA_eta_RL_per_heavyatom;ETA_psi_1;ETA_shape_p;ETA_shape_x;ETA_shape_y;FCSP3;FNSA1;FNSA2_per_heavyatom;FNSA3;FNSA4;FNSA5;FPSA1;FPSA2_per_heavyatom;FPSA3;FPSA4;FPSA5;FilterItLogS;GATS1Z;GATS1are;GATS1c;GATS1d;GATS1dv;GATS1i;GATS1m;GATS1p;GATS1pe;GATS1s;GATS1se;GATS1v;GATS2Z;GATS2are;GATS2c;GATS2d;GATS2dv;GATS2i;GATS2m;GATS2p;GATS2pe;GATS2s;GATS2se;GATS2v;GATS3Z;GATS3are;GATS3c;GATS3d;GATS3dv;GATS3i;GATS3m;GATS3p;GATS3pe;GATS3s;GATS3se;GATS3v;GATS4Z;GATS4are;GATS4c;GATS4d;GATS4dv;GATS4i;GATS4m;GATS4p;GATS4pe;GATS4s;GATS4se;GATS4v;GATS5Z;GATS5are;GATS5c;GATS5d;GATS5dv;GATS5i;GATS5m;GATS5p;GATS5pe;GATS5s;GATS5se;GATS5v;GATS6Z;GATS6are;GATS6c;GATS6d;GATS6dv;GATS6i;GATS6m;GATS6p;GATS6pe;GATS6s;GATS6se;GATS6v;GATS7Z;GATS7are;GATS7c;GATS7d;GATS7dv;GATS7i;GATS7m;GATS7p;GATS7pe;GATS7s;GATS7se;GATS7v;GATS8Z;GATS8are;GATS8c;GATS8d;GATS8dv;GATS8i;GATS8m;GATS8p;GATS8pe;GATS8s;GATS8se;GATS8v;GGI1_per_heavyatom;GGI10_per_heavyatom;GGI2_per_heavyatom;GGI3_per_heavyatom;GGI4_per_heavyatom;GGI5_per_heavyatom;GGI6_per_heavyatom;GGI7_per_heavyatom;GGI8_per_heavyatom;GGI9_per_heavyatom;GRAV_per_heavyatom;GRAVH_per_heavyatom;GRAVHp_per_heavyatom;GRAVp_per_heavyatom;GeomDiameter_per_heavyatom;GeomPetitjeanIndex;GeomRadius_per_heavyatom;GeomShapeIndex;GhoseFilter;HybRatio;IC0;IC1;IC2_per_heavyatom;IC3_per_heavyatom;IC4_per_heavyatom;IC5_per_heavyatom;JGI1;JGI10;JGI2;JGI3;JGI4;JGI5;JGI6;JGI7;JGI8;JGI9;JGT10;Kier1_per_heavyatom;Kier2_per_heavyatom;Kier3_per_heavyatom;LabuteASA_per_heavyatom;Lipinski;LogEE_A_per_heavyatom;LogEE_D_per_heavyatom;LogEE_Dt;LogEE_DzZ_per_heavyatom;LogEE_Dzare_per_heavyatom;LogEE_Dzi_per_heavyatom;LogEE_Dzm_per_heavyatom;LogEE_Dzp_per_heavyatom;LogEE_Dzpe_per_heavyatom;LogEE_Dzse_per_heavyatom;LogEE_Dzv_per_heavyatom;MATS1Z;MATS1are;MATS1c;MATS1d;MATS1dv;MATS1i;MATS1m;MATS1p;MATS1pe;MATS1s;MATS1se;MATS1v;MATS2Z;MATS2are;MATS2c;MATS2d;MATS2dv;MATS2i;MATS2m;MATS2p;MATS2pe;MATS2s;MATS2se;MATS2v;MATS3Z;MATS3are;MATS3c;MATS3d;MATS3dv;MATS3i;MATS3m;MATS3p;MATS3pe;MATS3s;MATS3se;MATS3v;MATS4Z;MATS4are;MATS4c;MATS4d;MATS4dv;MATS4i;MATS4m;MATS4p;MATS4pe;MATS4s;MATS4se;MATS4v;MATS5Z;MATS5are;MATS5c;MATS5d;MATS5dv;MATS5i;MATS5m;MATS5p;MATS5pe;MATS5s;MATS5se;MATS5v;MATS6Z;MATS6are;MATS6c;MATS6d;MATS6dv;MATS6i;MATS6m;MATS6p;MATS6pe;MATS6s;MATS6se;MATS6v;MATS7Z;MATS7are;MATS7c;MATS7d;MATS7dv;MATS7i;MATS7m;MATS7p;MATS7pe;MATS7s;MATS7se;MATS7v;MATS8Z;MATS8are;MATS8c;MATS8d;MATS8dv;MATS8i;MATS8m;MATS8p;MATS8pe;MATS8s;MATS8se;MATS8v;MDEC-22;MDEC-23;MDEC-33;MIC0;MIC1;MIC2;MIC3;MIC4;MIC5;MID_per_heavyatom;MID_C_per_heavyatom;MID_N_per_heavyatom;MID_O_per_heavyatom;MID_X;MID_h_per_heavyatom;MOMI-X_per_heavyatom;MOMI-Y_per_heavyatom;MOMI-Z_per_heavyatom;MPC10;MPC2;MPC3;MPC4;MPC5;MPC6;MPC7;MPC8;MPC9;MW_per_heavyatom;MWC01;MWC02_per_heavyatom;MWC03_per_heavyatom;MWC04_per_heavyatom;MWC05_per_heavyatom;MWC06_per_heavyatom;MWC07_per_heavyatom;MWC08_per_heavyatom;MWC09_per_heavyatom;MWC10_per_heavyatom;MZ;Mare;Mi;Mm;Mor01;Mor01m;Mor01p;Mor01se;Mor01v;Mor02;Mor02m;Mor02p;Mor02se;Mor02v;Mor03;Mor03m;Mor03p;Mor03se;Mor03v;Mor04;Mor04m;Mor04p;Mor04se;Mor04v;Mor05;Mor05m;Mor05p;Mor05se;Mor05v;Mor06;Mor06m;Mor06p;Mor06se;Mor06v;Mor07;Mor07m;Mor07p;Mor07se;Mor07v;Mor08;Mor08m;Mor08p;Mor08se;Mor08v;Mor09;Mor09m;Mor09p;Mor09se;Mor09v;Mor10;Mor10m;Mor10p;Mor10se;Mor10v;Mor11;Mor11m;Mor11p;Mor11se;Mor11v;Mor12;Mor12m;Mor12p;Mor12se;Mor12v;Mor13;Mor13m;Mor13p;Mor13se;Mor13v;Mor14;Mor14m;Mor14p;Mor14se;Mor14v;Mor15;Mor15m;Mor15p;Mor15se;Mor15v;Mor16;Mor16m;Mor16p;Mor16se;Mor16v;Mor17;Mor17m;Mor17p;Mor17se;Mor17v;Mor18;Mor18m;Mor18p;Mor18se;Mor18v;Mor19;Mor19m;Mor19p;Mor19se;Mor19v;Mor20;Mor20m;Mor20p;Mor20se;Mor20v;Mor21;Mor21m;Mor21p;Mor21se;Mor21v;Mor22;Mor22m;Mor22p;Mor22se;Mor22v;Mor23;Mor23m;Mor23p;Mor23se;Mor23v;Mor24;Mor24m;Mor24p;Mor24se;Mor24v;Mor25;Mor25m;Mor25p;Mor25se;Mor25v;Mor26;Mor26m;Mor26p;Mor26se;Mor26v;Mor27;Mor27m;Mor27p;Mor27se;Mor27v;Mor28;Mor28m;Mor28p;Mor28se;Mor28v;M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diff --git a/atomsci/ddm/docs/PARAMETERS.md b/atomsci/ddm/docs/PARAMETERS.md index aa98e952..630f07e7 100644 --- a/atomsci/ddm/docs/PARAMETERS.md +++ b/atomsci/ddm/docs/PARAMETERS.md @@ -16,7 +16,6 @@ The AMPL pipeline contains many parameters and options to fit models and make pr - [Hybrid model](#Hybrid-model) - [Splitting](#Splitting) - [Transformers](#Transformers) - - [UMAP](#UMAP) - [XGBoost](#XGBoost) - [Additional DeepChem Models](#Auto-DCModels) - [Model Saving](#Model-Saving) @@ -626,16 +625,17 @@ the model will train for max_epochs regardless of validation error.| ||| |-|-| -|*Description:*|type of transformation for the features| -|*Default:*|normalization| -|*Type:*|Choice| +|*Description:*|type of transformation for the features. Choices are {"normalization", "RobustScaler", "PowerTransformer", "Identity"}.| +|*Default:*|"normalizaton"| +|*Type:*|choice| - **response\_transform\_type** ||| |-|-| -|*Description:*|type of transformation for the response column (defaults to "normalization") TODO: Not currently implemented| -|*Default:*|normalization| +|*Description:*|type of transformation for the response column. Choices are {"normalization"}| +|*Default:*|"normalization"| +|*Type:*|choice| - **weight\_transform\_type** @@ -673,46 +673,63 @@ the model will train for max_epochs regardless of validation error.| |*Default:*|TRUE| |*Type:*|Bool| ---- - - -## UMAP - -- **umap\_dim** +- **robustscaler_with_centering** ||| |-|-| -|*Description:*|Dimension of projected feature space, if UMAP transformation is requested. Can be input as a comma separated list for hyperparameter search (e.g. '2,6,10').| -|*Default:*|10| - -- **umap\_metric** +|*Description:*|If `True`, center the data before scaling. This will cause `transform` to raise an exception when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.| +|*Default:*|TRUE| +|*Type:*|Bool| + +- **robustscaler_with_scaling** ||| |-|-| -|*Description:*|Distance metric used, if UMAP transformation is requested. Can be input as a comma separated list for hyperparameter search (e.g. 'euclidean','cityblock')| -|*Default:*|euclidean| - -- **umap\_min\_dist** +|*Description:*|If `True`, scale the data to interquartile range.| +|*Default:*|TRUE| +|*Type:*|Bool| + + - **robustscaler_quartile_range** ||| |-|-| -|*Description:*|Minimum distance used in UMAP projection, if UMAP transformation is requested. Can be input as a comma separated list for hyperparameter search (e.g. '0.01,0.02,0.05')| -|*Default:*|0.05| - -- **umap\_neighbors** +|*Description:*|Quantile range used to calculate `scale_`. By default this is equal to the IQR, i.e., `q_min` is the first quantile and `q_max` is the third quantile. `(q_min, q_max), 0.0 < q_min < q_max < 100.0`| +|*Default:*|(25.0, 75.0)| +|*Type:*|List| + + - **robustscaler_unit_variance** ||| |-|-| -|*Description:*|Number of nearest neighbors used in UMAP projection, if UMAP transformation is requested. Can be input as a comma separated list for hyperparameter search (e.g. '10,20,30')| -|*Default:*|20| +|*Description:*|If `True`, scale data so that normally distributed features have a variance of 1. In general, if the difference between the x-values of `q_max` and `q_min` for a standard normal distribution is greater than 1, the dataset will be scaled down. If less than 1, the dataset will be scaled up.| +|*Default:*|FALSE| +|*Type:*|bool| + + - **powertransformer_method** -- **umap\_targ\_wt** +||| +|-|-| +|*Description:*|The power transform method. Available methods are: ‘yeo-johnson’ , works with positive and negative values ‘box-cox’, only works with strictly positive values. Choices are {"yeo-johnson", "box-cox"}| +|*Default:*|"yeo-johnson"| +|*Type:*|choice| + + - **powertransformer_standardize** ||| |-|-| -|*Description:*|Weight given to training set response values in UMAP projection, if UMAP transformation is requested. Can be input as a comma separated list for hyperparameter search (e.g. '0.0,0.1,0.2')| -|*Default:*|0.0| +|*Description:*|Set to True to apply zero-mean, unit-variance normalization to the transformed output.| +|*Default:*|TRUE| +|*Type:*|Bool| + + - **imputer_strategy** +||| +|-|-| +|*Description:*|This sets the imputer strategy for the SimpleImputer for use with PowerTransformer or RobustScaler. Choices are {"mean", "median", "most_frequent"}| +|*Default:*|"mean"| +|*Type:*|choice| + + --- diff --git a/atomsci/ddm/pipeline/compare_models.py b/atomsci/ddm/pipeline/compare_models.py index 8f55d9be..0f30bf9b 100644 --- a/atomsci/ddm/pipeline/compare_models.py +++ b/atomsci/ddm/pipeline/compare_models.py @@ -691,202 +691,6 @@ def get_best_models_info(col_names=None, bucket='public', pred_type="regression" top_models_df.to_csv(os.path.join(output_dir, 'best_models_metadata_%s.csv' % shortened_key), index=False) return top_models_df - -# TODO: This function looks like work in progress, should we delete it? -''' -#--------------------------------------------------------------------------------------------------------- -def _get_best_grouped_models_info(collection='pilot_fixed', pred_type='regression', top_n=1, subset='test'): - """Get results for models in the given collection.""" - - if not mlmt_supported: - print("Model tracker not supported in your environment; can examine models saved in filesystem only.") - return - - res_dir = '/usr/local/data/%s_perf' % collection - plt_dir = '%s/Plots' % res_dir - os.makedirs(plt_dir, exist_ok=True) - res_files = os.listdir(res_dir) - suffix = '_%s_model_perf_metrics.csv' % collection - - if pred_type == 'regression': - metric_type = 'r2_score' - else: - metric_type = 'roc_auc_score' - for res_file in res_files: - try: - if not res_file.endswith(suffix): - continue - res_path = os.path.join(res_dir, res_file) - - res_df = pd.read_csv(res_path, index_col=False) - res_df['combo'] = ['%s/%s' % (m,f) for m, f in zip(res_df.model_type.values, res_df.featurizer.values)] - dset_name = res_file.replace(suffix, '') - datasets.append(dset_name) - res_df['dataset'] = dset_name - print(dset_name) - res_df = res_df.sort_values('{0}_{1}'.format(metric_type, subset), ascending=False) - res_df['model_type/feat'] = ['%s/%s' % (m,f) for m, f in zip(res_df.model_type.values, res_df.featurizer.values)] - res_df = res_df.sort_values('{0}_{1}'.format(metric_type, subset), ascending=False) - grouped_df = res_df.groupby('model_type/feat').apply( - lambda t: t.head(top_n) - ).reset_index(drop=True) - top_grouped_models.append(grouped_df) - top_combo = res_df['model_type/feat'].values[0] - top_combo_dsets.append(top_combo + dset_name.lstrip('ATOM_GSK_dskey')) - top_score = res_df['{0}_{1}'.format(metric_type, subset)].values[0] - top_model_feat.append(top_combo) - top_scores.append(top_score) - num_samples.append(res_df['Dataset Size'][0]) - -#------------------------------------------------------------------------------------------------------------------ -def get_umap_nn_model_perf_table(dataset_key, bucket, collection_name, pred_type='regression'): - """Load performance metrics from model tracker for all NN models with the given prediction_type saved in - the model tracker DB under a given collection that were trained against a particular dataset. Show - parameter settings for UMAP transformer for models where they are available. - - Args: - dataset_key (str): Dataset key for training dataset. - - bucket (str): Dataset bucket for training dataset. - - collection_name (str): Name of model tracker collection to search for models. - - pred_type (str): Prediction type ('classification' or 'regression') of models to query. - - Returns: - pd.DataFrame: Table of model performance metrics. - - """ - if not mlmt_supported: - print("Model tracker not supported in your environment; can examine models saved in filesystem only.") - return None - - query_params = { - "match_metadata": { - "training_dataset.bucket": bucket, - "training_dataset.dataset_key": dataset_key, - "model_parameters.model_type" : "NN", - "model_parameters.prediction_type" : pred_type - }, - - "match_metrics": { - "metrics_type": "training", # match only training metrics - "label": "best", - }, - } - query_params['match_metadata'].update(other_filters) - - print("Finding models trained on %s dataset %s" % (bucket, dataset_key)) - mlmt_client = dsf.initialize_model_tracker() - metadata_list = mlmt_client.model.query_model_metadata( - collection_name=collection_name, - query_params=query_params, - ).result() - if metadata_list == []: - print("No matching models returned") - return - else: - print("Found %d matching models" % len(metadata_list)) - - model_uuid_list = [] - learning_rate_list = [] - dropouts_list = [] - layer_sizes_list = [] - featurizer_list = [] - best_epoch_list = [] - max_epochs_list = [] - - feature_transform_type_list = [] - umap_dim_list = [] - umap_targ_wt_list = [] - umap_neighbors_list = [] - umap_min_dist_list = [] - - subsets = ['train', 'valid', 'test'] - - if pred_type == 'regression': - sort_metric = 'r2_score' - metrics = ['r2_score', 'rms_score', 'mae_score'] - else: - sort_metric = 'roc_auc_score' - metrics = ['roc_auc_score', 'prc_auc_score', 'matthews_cc', 'kappa', 'confusion_matrix'] - score_dict = {} - for subset in subsets: - score_dict[subset] = {} - for metric in metrics: - score_dict[subset][metric] = [] - - for metadata_dict in metadata_list: - model_uuid = metadata_dict['model_uuid'] - #print("Got metadata for model UUID %s" % model_uuid) - - # Get model metrics for this model - metrics_dicts = metadata_dict['training_metrics'] - #print("Got %d metrics dicts for model %s" % (len(metrics_dicts), model_uuid)) - if len(metrics_dicts) < 3: - print("Got no or incomplete metrics for model %s, skipping..." % model_uuid) - continue - if len(metrics_dicts) > 3: - raise Exception('Got more than one set of best epoch metrics for model %s' % model_uuid) - subset_metrics = {} - for metrics_dict in metrics_dicts: - subset = metrics_dict['subset'] - subset_metrics[subset] = metrics_dict['prediction_results'] - - model_uuid_list.append(model_uuid) - model_params = metadata_dict['model_parameters'] - model_type = model_params['model_type'] - if model_type != 'NN': - continue - featurizer = model_params['featurizer'] - featurizer_list.append(featurizer) - feature_transform_type = metadata_dict['training_dataset']['feature_transform_type'] - feature_transform_type_list.append(feature_transform_type) - nn_params = metadata_dict['nn_specific'] - max_epochs_list.append(nn_params['max_epochs']) - best_epoch_list.append(nn_params['best_epoch']) - learning_rate_list.append(nn_params['learning_rate']) - layer_sizes_list.append(','.join(['%d' % s for s in nn_params['layer_sizes']])) - dropouts_list.append(','.join(['%.2f' % d for d in nn_params['dropouts']])) - for subset in subsets: - for metric in metrics: - score_dict[subset][metric].append(subset_metrics[subset][metric]) - if 'umap_specific' in metadata_dict: - umap_params = metadata_dict['umap_specific'] - umap_dim_list.append(umap_params['umap_dim']) - umap_targ_wt_list.append(umap_params['umap_targ_wt']) - umap_neighbors_list.append(umap_params['umap_neighbors']) - umap_min_dist_list.append(umap_params['umap_min_dist']) - else: - umap_dim_list.append(nan) - umap_targ_wt_list.append(nan) - umap_neighbors_list.append(nan) - umap_min_dist_list.append(nan) - - - perf_df = pd.DataFrame(dict( - model_uuid=model_uuid_list, - learning_rate=learning_rate_list, - dropouts=dropouts_list, - layer_sizes=layer_sizes_list, - featurizer=featurizer_list, - best_epoch=best_epoch_list, - max_epochs=max_epochs_list, - feature_transform_type=feature_transform_type_list, - umap_dim=umap_dim_list, - umap_targ_wt=umap_targ_wt_list, - umap_neighbors=umap_neighbors_list, - umap_min_dist=umap_min_dist_list )) - for subset in subsets: - for metric in metrics: - metric_col = '%s_%s' % (metric, subset) - perf_df[metric_col] = score_dict[subset][metric] - sort_by = '%s_valid' % sort_metric - - perf_df = perf_df.sort_values(sort_by, ascending=False) - return perf_df -''' - #------------------------------------------------------------------------------------------------------------------ def get_tarball_perf_table(model_tarball, pred_type='classification'): """Retrieve model metadata and performance metrics for a model saved as a tarball (.tar.gz) file. diff --git a/atomsci/ddm/pipeline/featurization.py b/atomsci/ddm/pipeline/featurization.py index 75e31df8..9df02e83 100644 --- a/atomsci/ddm/pipeline/featurization.py +++ b/atomsci/ddm/pipeline/featurization.py @@ -23,6 +23,9 @@ from rdkit.Chem import Descriptors from rdkit.ML.Descriptors import MoleculeDescriptors +from sklearn.preprocessing import RobustScaler, PowerTransformer +from sklearn.impute import SimpleImputer +from sklearn.pipeline import Pipeline subclassed_mordred_classes = ['EState', 'MolecularDistanceEdge'] try: from mordred import Calculator, descriptors @@ -348,7 +351,7 @@ def compute_all_mordred_descrs(mols, max_cpus=None, quiet=True): res_df = calc.pandas(mols, quiet=quiet, nproc=max_cpus) log.debug("Done computing Mordred descriptors") try: - res_df = res_df.fill_missing().applymap(float) + res_df = res_df.fill_missing().map(float) return res_df except ValueError: log.error('Mordred descriptor calculation failed in MordredDataFrame.fill_missing') @@ -637,12 +640,13 @@ def get_feature_count(self): raise NotImplementedError # **************************************************************************************** - def create_feature_transformer(self, dataset): + def create_feature_transformer(self, dataset, params): """Fit a scaling and centering transformation to the feature matrix of the given dataset, and return a DeepChem transformer object holding its parameters. Args: dataset (deepchem.Dataset): featurized dataset + params (Namespace): Contains parameters used to instantiate the featurizer. Returns: Empty list @@ -836,12 +840,13 @@ def featurize_data(self, dset_df, params, contains_responses): return features, ids, vals, attr, w, featurized_dset_df # **************************************************************************************** - def create_feature_transformer(self, dataset): + def create_feature_transformer(self, dataset, params): """Fit a scaling and centering transformation to the feature matrix of the given dataset, and return a DeepChem transformer object holding its parameters. Args: dataset (deepchem.Dataset): featurized dataset + params (Namespace): Contains parameters used to instantiate the featurizer. Returns: Empty list since we will not be transforming the features of a DynamicFeaturization object @@ -1183,12 +1188,13 @@ def featurize_data(self, dset_df, params, contains_responses): raise NotImplementedError # **************************************************************************************** - def create_feature_transformer(self, dataset): + def create_feature_transformer(self, dataset, params): """Fit a scaling and centering transformation to the feature matrix of the given dataset, and return a DeepChem transformer object holding its parameters. Args: dataset (deepchem.Dataset): featurized dataset + params (Namespace): Contains parameters used to instantiate the featurizer. """ # Leave it to subclasses to determine if features should be scaled and centered. @@ -1621,17 +1627,56 @@ def get_feature_count(self): return len(self.get_feature_columns()) # **************************************************************************************** - def create_feature_transformer(self, dataset): + def create_feature_transformer(self, dataset, params): """Fit a scaling and centering transformation to the feature matrix of the given dataset, and return a DeepChem transformer object holding its parameters. Args: dataset (deepchem.Dataset): featurized dataset + params (Namespace): Contains parameters used to instantiate the featurizer. Returns: (list of DeepChem transformer objects): list of transformers for the feature matrix """ - transformers_x = [trans.NormalizationTransformerMissingData(transform_X=True, dataset=dataset)] + if params.feature_transform_type == 'normalization': + transformers_x = [trans.NormalizationTransformerMissingData(transform_X=True, dataset=dataset)] + elif params.feature_transform_type == 'RobustScaler': + # keep_empty_features is set to true so that feature counts do not change + imputer = SimpleImputer(strategy=params.imputer_strategy, keep_empty_features=True) + # Check quartile_range has length 2 and convert it into a tuple + quartiles = params.robustscaler_quartile_range + assert len(quartiles) == 2, f'robustscaler_quartile_range must have length 2, got {quartiles}.' + quartiles = (quartiles[0], quartiles[1]) + robust_scaler = RobustScaler( + with_centering=params.robustscaler_with_centering, + with_scaling=params.robustscaler_with_scaling, + quantile_range=quartiles, + unit_variance=params.robustscaler_unit_variance + ) + pipeline = Pipeline([('SimpleImputer', imputer), ('RobustScaler', robust_scaler)]) + transformers_x = [ + trans.SklearnPipelineWrapper(transform_X=True, dataset=dataset, + sklearn_pipeline=pipeline) + ] + elif params.feature_transform_type == 'PowerTransformer': + # keep_empty_features is set to true so that feature counts do not change + imputer = SimpleImputer(strategy=params.imputer_strategy, keep_empty_features=True) + power_transformer = PowerTransformer( + method=params.powertransformer_method, + standardize=params.powertransformer_standardize + ) + pipeline = Pipeline([('SimpleImputer', imputer), ('PowerTransformer', power_transformer)]) + transformers_x = [ + trans.SklearnPipelineWrapper(transform_X=True, dataset=dataset, + sklearn_pipeline=pipeline) + ] + elif params.feature_transform_type == 'Identity': + transformers_x = [] + else: + raise ValueError(( + "feature_transform_type must be normalization, RobustScaler, " + f"PowerTransformer, or Identity. Got {params.feature_transform_type}")) + return transformers_x @@ -1907,6 +1952,9 @@ def compute_descriptors(self, smiles_df, params): if not mordred_supported: raise Exception("mordred package needs to be installed to use Mordred descriptors") desc_df, is_valid = self.compute_mordred_descriptors(smiles_df[params.smiles_col].values, params) + if descr_scaled: + desc_df = self.scale_by_heavyatomcount_and_log_scale(desc_df, params.descriptor_type) + desc_df = desc_df[descr_cols] # Add the ID and SMILES columns to the returned data frame ret_df = smiles_df[is_valid][[params.id_col, params.smiles_col]].reset_index(drop=True) @@ -1917,6 +1965,10 @@ def compute_descriptors(self, smiles_df, params): elif descr_source == 'rdkit': desc_df, is_valid = self.compute_rdkit_descriptors(smiles_df, smiles_col = params.smiles_col) + if descr_scaled: + desc_df = self.scale_by_heavyatomcount_and_log_scale(desc_df, params.descriptor_type) + # RDKit features do not contain log scale features + desc_df = desc_df[descr_cols] # Add the ID and SMILES columns to the returned data frame ret_df = smiles_df[is_valid][[params.id_col, params.smiles_col]].reset_index(drop=True) @@ -2040,6 +2092,61 @@ def scale_moe_descriptors(self, desc_df, descr_type): scaled_df=pd.concat(scaled_cols, axis=1) return scaled_df.copy() + # **************************************************************************************** + def scale_by_heavyatomcount_and_log_scale(self, desc_df, descr_type, heavy_atom_col=''): + """Scale selected descriptors computed by rdkit or mordred by dividing their values by the heavy atom count per molecule. + + Args: + desc_df (DataFrame): Data frame containing computed descriptors. + + descr_type (str): Descriptor type, used to look up expected set of descriptor columns. + + heavy_atom_col (str): Column containing the heavy atom count. Default value is '', which will try + to infer the heavy atom count column based on descr_type. + + Returns: + scaled_df (DataFrame): Data frame with scaled descriptors. + + Raises: + ValueError: If there are negative feature values in a column that needs log scaling + + """ + cls = self.__class__ + descr_cols = cls.desc_type_cols[descr_type] + if heavy_atom_col != '': + ha_count = desc_df[heavy_atom_col].values + else: + if 'HeavyAtomCount' in descr_cols: + ha_count = desc_df.HeavyAtomCount.values + elif 'nHeavyAtom' in descr_cols: + ha_count = desc_df.nHeavyAtom.values + else: + raise RuntimeError("Could not infer heavy atom column. " + "Should be HeavyAtomCount or nHeavyAtom or set using the heavy_atom_col parameter") + + unscaled_desc_cols = [col.replace('_per_heavyatom', '') for col in descr_cols] + unscaled_desc_cols = [col.replace('_log_scaled', '') for col in unscaled_desc_cols] + nondesc_cols = list(set(desc_df.columns.values) - set(unscaled_desc_cols)) + scaled_cols=[desc_df[nondesc_cols].copy()] + for scaled_col, unscaled_col in zip(descr_cols, unscaled_desc_cols): + if scaled_col.endswith('_per_heavyatom'): + tmp_col=desc_df[unscaled_col] / ha_count + tmp_col=tmp_col.rename(scaled_col) + scaled_cols.append(tmp_col) + elif scaled_col.endswith('_log_scaled'): + feat_vals = desc_df[unscaled_col] + if any(feat_vals < 0): + raise ValueError(f"Cannot log scale {unscaled_col} because of negative values in feature.") + tmp_col=np.log(feat_vals) + tmp_col=tmp_col.rename(scaled_col) + scaled_cols.append(tmp_col) + else: + tmp_col=desc_df[unscaled_col] + tmp_col=tmp_col.rename(scaled_col) + scaled_cols.append(tmp_col) + scaled_df=pd.concat(scaled_cols, axis=1) + return scaled_df.copy() + # **************************************************************************************** def __str__(self): """Returns a human-readable description of this Featurization object. diff --git a/atomsci/ddm/pipeline/model_datasets.py b/atomsci/ddm/pipeline/model_datasets.py index cc7c18f1..795a9f7d 100644 --- a/atomsci/ddm/pipeline/model_datasets.py +++ b/atomsci/ddm/pipeline/model_datasets.py @@ -23,6 +23,25 @@ logging.basicConfig(format='%(asctime)-15s %(message)s') +# ***************************************************************************************** +def create_and_load_model_dataset(params, ds_client=None): + """Factory function for creating and loading a ModelDataset object. + + Args: + params (Namespace object): contains all parameter information. + + ds_client (Datastore client) + Returns: + (ModelDataset): instantiated ModelDataset subclass specified by params, with data loaded + """ + featurization = feat.create_featurization(params) + dataset = create_model_dataset(params, featurization, ds_client=None) + + # Load featurized data (this will use scaled_descriptors if available) + dataset.get_featurized_data() + + return dataset + # **************************************************************************************** def create_model_dataset(params, featurization, ds_client=None): """Factory function for creating DatastoreDataset or FileDataset objects. diff --git a/atomsci/ddm/pipeline/model_tracker.py b/atomsci/ddm/pipeline/model_tracker.py index 61053c7e..9f6a3d85 100644 --- a/atomsci/ddm/pipeline/model_tracker.py +++ b/atomsci/ddm/pipeline/model_tracker.py @@ -459,9 +459,8 @@ def convert_metadata(old_metadata): ("descriptor_specific", 'DescriptorSpecific'), ("nn_specific", "NNSpecific"), ("xgb_specific", "xgbSpecific"), - ("umap_specific", "UmapSpecific"), - ] + for (nkey, okey) in map_keys: value = model_metadata.get(okey) if value is not None: diff --git a/atomsci/ddm/pipeline/model_wrapper.py b/atomsci/ddm/pipeline/model_wrapper.py index 59bbda13..03626840 100644 --- a/atomsci/ddm/pipeline/model_wrapper.py +++ b/atomsci/ddm/pipeline/model_wrapper.py @@ -412,7 +412,10 @@ def create_transformers(self, training_datasets): for k, td in training_datasets.items(): self.transformers[k] = self._create_output_transformers(td) - self.transformers_x[k] = self._create_feature_transformers(td) + if self.params.feature_transform_path is None: + self.transformers_x[k] = self._create_feature_transformers(td) + else: + self.transformers_x[k] = self.load_feature_transformers_from_pkl(self.params.feature_transform_path) # Set up transformers for weights, if needed self.transformers_w[k] = trans.create_weight_transformers(self.params, td) @@ -488,6 +491,40 @@ def reload_transformers(self): # **************************************************************************************** + def load_feature_transformers_from_pkl(self, transformer_pkl_path): + """Loads feature transformer from pkl path + This loads transformers_x from a pkl created using + generate_transformers.build_and_save_feature_transformers_from_csvs. This also + overwrites any parameters set when creating the transfomers saved at + transformer_pkl_path. + + Args: + transformer_pkl_path (str): Path of saved transformer_pkl. + + Returns: + None + + """ + with open(transformer_pkl_path, 'rb') as transformers_pkl_file: + saved_transformers = pickle.load(transformers_pkl_file) + + # check that the loaded transformers are for the same features + if self.params.featurizer != saved_transformers['params']['featurizer']: + raise ValueError((f"Loaded transformers do not match featurizer. Expected {self.params.featurizer}," + f" got {saved_transformers['params']['featurizer']}")) + if self.params.descriptor_type != saved_transformers['params']['descriptor_type']: + raise ValueError((f"Loaded transformers do not match descriptor_type. Expected {self.params.descriptor_type}," + f" got {saved_transformers['params']['descriptor_type']}")) + + # update self.params with the loaded transformer data + self.params.__dict__.update(saved_transformers['params']) + + return saved_transformers['transformers_x'] + + + # **************************************************************************************** + + def transform_dataset(self, dataset, fold): """Transform the responses and/or features in the given DeepChem dataset using the current transformers. @@ -523,10 +560,7 @@ def get_num_features(self): the number of dimensions of the feature space, taking both featurization method and transformers into account. """ - if self.params.feature_transform_type == 'umap': - return self.params.umap_dim - else: - return self.featurization.get_feature_count() + return self.featurization.get_feature_count() # **************************************************************************************** diff --git a/atomsci/ddm/pipeline/parameter_parser.py b/atomsci/ddm/pipeline/parameter_parser.py index b1a6cdff..9fec8d0d 100644 --- a/atomsci/ddm/pipeline/parameter_parser.py +++ b/atomsci/ddm/pipeline/parameter_parser.py @@ -530,7 +530,7 @@ def get_list_args(self): # Parameters that may take lists of values, usually but not always in the context of a hyperparam search convert_to_float_list = {'dropouts','weight_init_stddevs','bias_init_consts','learning_rate', - 'umap_targ_wt', 'umap_min_dist', 'dropout_list','weight_decay_penalty', + 'dropout_list','weight_decay_penalty', 'xgb_learning_rate', 'xgb_gamma', 'xgb_alpha', @@ -538,10 +538,11 @@ def get_list_args(self): "xgb_min_child_weight", "xgb_subsample", "xgb_colsample_bytree", - "ki_convert_ratio" + "ki_convert_ratio", + "robustscaler_quartile_range" } convert_to_int_list = {'layer_sizes','rf_max_features','rf_estimators', 'rf_max_depth', - 'umap_dim', 'umap_neighbors', 'layer_nums', 'node_nums', + 'layer_nums', 'node_nums', 'xgb_max_depth', 'xgb_n_estimators', 'seed'}.union(all_auto_int_lists()) convert_to_numeric_list = convert_to_float_list | convert_to_int_list keep_as_list = {'dropouts','weight_init_stddevs','bias_init_consts', @@ -557,9 +558,9 @@ def get_list_args(self): 'xgb_max_depth', 'xgb_n_estimators' } convert_to_str_list = \ - {'response_cols','model_type','featurizer','splitter','umap_metric','weight_decay_penalty_type','descriptor_type'} + {'response_cols','model_type','featurizer','splitter','weight_decay_penalty_type','descriptor_type'} not_a_str_list_outside_of_hyperparams = \ - {'model_type','featurizer','splitter','umap_metric','weight_decay_penalty_type','descriptor_type'} + {'model_type','featurizer','splitter','weight_decay_penalty_type','descriptor_type'} #********************************************************************************************************** def to_str(params_obj): @@ -781,8 +782,14 @@ def dict_to_list(inp_dictionary,replace_spaces=False): temp_list_to_command_line = [] # Special case handling for arguments that are False or True by default - default_false = ['previously_split','use_shortlist','datastore', 'save_results','verbose', 'hyperparam', 'split_only', 'is_ki', 'production', 'embedding_and_features'] - default_true = ['transformers','previously_featurized','uncertainty', 'rerun'] + default_false = ['previously_split','use_shortlist','datastore', + 'save_results','verbose', 'hyperparam', 'split_only', 'is_ki', 'production', + 'embedding_and_features', + 'robustscaler_unit_variance'] + default_true = ['transformers','previously_featurized','uncertainty', 'rerun', + 'robustscaler_with_centering', 'robustscaler_with_scaling', + 'powertransformer_standardize'] + for key, value in inp_dictionary.items(): if key in default_false: true_options = ['True','true','ture','TRUE','Ture'] @@ -1281,11 +1288,13 @@ def get_parser(): # ********************************************************************************************************** # model_building_parameters: transformers parser.add_argument( - '--feature_transform_type', dest='feature_transform_type', choices=['normalization', 'umap'], + '--feature_transform_type', dest='feature_transform_type', + choices=['normalization', 'RobustScaler', 'PowerTransformer', 'Identity'], default='normalization', help='type of transformation for the features') parser.add_argument( '--response_transform_type', dest='response_transform_type', default='normalization', - help='type of normalization for the response column TODO: Not currently implemented') + choices=['normalization'], + help='type of normalization for the response column.') parser.add_argument( '--weight_transform_type', dest='weight_transform_type', choices=[None, 'None', 'balancing'], default=None, help='type of normalization for the weights') @@ -1305,28 +1314,59 @@ def get_parser(): help='Boolean switch for using transformation on regression output. Default is True') parser.set_defaults(transformers=True) - # ********************************************************************************************************** - # model_building_parameters: UMAP - parser.add_argument( - '--umap_dim', dest='umap_dim', required=False, default='10', - help='Dimension of projected feature space, if UMAP transformation is requested. Can be input as a comma ' - 'separated list for hyperparameter search (e.g. \'2,6,10\').') - parser.add_argument( - '--umap_metric', dest='umap_metric', required=False, default='euclidean', - help='Distance metric used, if UMAP transformation is requested. Can be input as a comma separated list ' - 'for hyperparameter search (e.g. \'euclidean\',\'cityblock\')') + # Load transformer parser.add_argument( - '--umap_min_dist', dest='umap_min_dist', required=False, default='0.05', - help='Minimum distance used in UMAP projection, if UMAP transformation is requested. Can be input as a ' - 'comma separated list for hyperparameter search (e.g. \'0.01,0.02,0.05\')') - parser.add_argument( - '--umap_neighbors', dest='umap_neighbors', required=False, default='20', - help='Number of nearest neighbors used in UMAP projection, if UMAP transformation is requested. Can be input ' - 'as a comma separated list for hyperparameter search (e.g. \'10,20,30\')') - parser.add_argument( - '--umap_targ_wt', dest='umap_targ_wt', required=False, default='0.0', - help='Weight given to training set response values in UMAP projection, if UMAP transformation is requested.' - ' Can be input as a comma separated list for hyperparameter search (e.g. \'0.0,0.1,0.2\')') + '--feature_transform_path', dest='feature_transform_path', + type=str, default=None, help='Path to a transformer pkl created using generate_transformers. This ' + 'will overwrite any relevant transformer parameters with values loaded from the pkl' + ) + + # RobustScaler parameters + parser.add_argument( + '--robustscaler_with_centering', action='store_false', + help='If True, center the data before scaling. ' + 'This will cause transform to raise an exception when attempted on sparse matrices, ' + 'because centering them entails building a dense matrix which in common use ' + 'cases is likely to be too large to fit in memory. Default is True') + parser.set_defaults(robustscaler_with_centering=True) + parser.add_argument( + '--robustscaler_with_scaling', action='store_false', + help='If True, scale the data to interquartile range. Default is True') + parser.set_defaults(robustscaler_with_scaling=True) + parser.add_argument( + '--robustscaler_quartile_range', type=str, default='25.0,75.0', + help='Quantile range used to calculate scale_. ' + 'By default this is equal to the IQR, i.e., ' + 'q_min is the first quantile and q_max is the third quantile. ' + '(q_min, q_max), 0.0 < q_min < q_max < 100.0. Default is "25.0,75.0"') + parser.add_argument( + '--robustscaler_unit_variance', action='store_true', + help='If True, scale data so that normally distributed features have a variance of 1. ' + 'In general, if the difference between the x-values of q_max and q_min for a standard ' + 'normal distribution is greater than 1, the dataset will be scaled down. ' + 'If less than 1, the dataset will be scaled up. Default is False.') + parser.set_defaults(robustscaler_unit_variance=False) + + # PowerTransformer parameters + parser.add_argument( + '--powertransformer_method', choices=['yeo-johnson', 'box-cox'], + default='yeo-johnson', + help='The power transform method. Available methods are: "yeo-johnson", ' + 'works with positive and negative values "box-cox", only works with strictly positive values. ' + 'Choices are {"yeo-johnson", "box-cox"}') + parser.add_argument( + '--powertransformer_standardize', action='store_false', + help='Set to True to apply zero-mean, unit-variance normalization to the transformed output. ' + 'Default is True.') + parser.set_defaults(powertransformer_standardize=True) + + # Sklearn parameters + parser.add_argument( + '--imputer_strategy', choices=['mean', 'median', 'most_frequent'], + default='mean', + help='This sets the imputer strategy for the SimpleImputer for use with' + 'PowerTransformer or RobustScaler.' + 'Choices are {"mean", "median", "most_frequent"}') # ********************************************************************************************************** # model_building_parameters: XGBoost diff --git a/atomsci/ddm/pipeline/perf_plots.py b/atomsci/ddm/pipeline/perf_plots.py index bd0b03f9..e6f0b9d4 100644 --- a/atomsci/ddm/pipeline/perf_plots.py +++ b/atomsci/ddm/pipeline/perf_plots.py @@ -12,6 +12,7 @@ import seaborn as sns import umap import sklearn.metrics as metrics +import warnings from sklearn.metrics import ConfusionMatrixDisplay, PrecisionRecallDisplay import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages @@ -86,7 +87,8 @@ def plot_pred_vs_actual(model, epoch_label='best', threshold=None, error_bars=Fa None """ if isinstance(model, str): - return plot_pred_vs_actual_from_file(model, plot_size=plot_size, external_training_data=external_training_data, error_bars=error_bars) + plot_pred_vs_actual_from_file(model, plot_size=plot_size, external_training_data=external_training_data, error_bars=error_bars) + return elif not isinstance(model, mp.ModelPipeline): raise ValueError('model must be either a ModelPipeline or a path to a saved model') params = model.params @@ -266,8 +268,7 @@ def plot_pred_vs_actual_from_df(pred_df, actual_col='avg_pIC50_actual', pred_col #------------------------------------------------------------------------------------------------------------------------ def plot_pred_vs_actual_from_file(model_path, external_training_data=None, plot_size=7, error_bars=False, threshold=None): - """Plot predicted vs actual values from a trained regression model from a model tarball. - This function only works for locally trained models; otherwise see the `predict_from_model` module. + """Plot predicted vs actual values from a trained regression model from a model tarball. This function only works for locally trained models; otherwise see the predict_from_model module. Args: model_path (str): Path to an AMPL model tar.gz file. @@ -276,7 +277,7 @@ def plot_pred_vs_actual_from_file(model_path, external_training_data=None, plot_ error_bars (bool): whether to plot uncertainty as shaded region above and below the predicted values. Returns: - None + Matplotlib Figure Effects: A matplotlib figure is displayed with subplots for each response column and train/valid/test subsets. @@ -286,100 +287,163 @@ def plot_pred_vs_actual_from_file(model_path, external_training_data=None, plot_ reload_dir = tempfile.mkdtemp() with tarfile.open(model_path, mode='r:gz') as tar: futils.safe_extract(tar, path=reload_dir) - + # reload metadata with open(os.path.join(reload_dir, 'model_metadata.json')) as f: - config=json.loads(f.read()) + config = json.loads(f.read()) - if config['model_parameters']['prediction_type']=='classification': - raise ValueError("plot_pred_vs_actual_from_file() should only be called for regression models. Please try plot_confusion_matrices() instead.") - - # load (featurized) data - dataset_dict=config['training_dataset'] + if config['model_parameters']['prediction_type'] == 'classification': + raise ValueError( + "plot_pred_vs_actual_from_file() should only be called for regression models. " + "Please try plot_confusion_matrices() instead." + ) + + # dataset info + dataset_dict = config['training_dataset'] if external_training_data is not None: - dataset_dict['dataset_key']=external_training_data - dataset_key=dataset_dict['dataset_key'] - dataset_name = os.path.splitext(os.path.basename(dataset_key))[0] + dataset_dict['dataset_key'] = external_training_data - is_featurized=False - AD_method=None + original_dataset_key = dataset_dict['dataset_key'] + dataset_name = os.path.splitext(os.path.basename(original_dataset_key))[0] + + response_cols = dataset_dict['response_cols'] + id_col = dataset_dict['id_col'] + smiles_col = dataset_dict['smiles_col'] + + # determine featurized path + is_featurized = False + AD_method = None model_type = config['model_parameters']['model_type'] featurizer = config['model_parameters']['featurizer'] - if featurizer in ['descriptors','computed_descriptors']: - desc=config['descriptor_specific']['descriptor_type'] - dataset_key=dataset_key.rsplit('/', maxsplit=1) - dataset_key=os.path.join(dataset_key[0], 'scaled_descriptors', dataset_key[1].replace('.csv',f'_with_{desc}_descriptors.csv')) - is_featurized=True + + dataset_key_to_read = original_dataset_key + if featurizer in ['descriptors', 'computed_descriptors']: + desc = config['descriptor_specific']['descriptor_type'] + parts = original_dataset_key.rsplit('/', maxsplit=1) + dataset_key_to_read = os.path.join( + parts[0], + 'scaled_descriptors', + parts[1].replace('.csv', f'_with_{desc}_descriptors.csv') + ) + is_featurized = True features_label = f"{desc} descriptors" else: features_label = f"{featurizer} features" - df=pd.read_csv(dataset_key) - - # reload split file - dataset_key=dataset_dict['dataset_key'] - split_dict=config['splitting_parameters'] + + # load dataframes + df = pd.read_csv(dataset_key_to_read) + orig_df = pd.read_csv(original_dataset_key) + + # sanity-check alignment and merge missing response columns + df = merge_response_cols_from_original( + df, + orig_df, + id_col=id_col, + response_cols=response_cols, + max_missing_frac=0.01, + error_on_extra_feat_ids=False, + coerce_id_to_str=True, + sample_n=20, + ) + + # reload split file (use original dataset location) + split_dict = config['splitting_parameters'] split_uuid = split_dict['split_uuid'] - splitter=split_dict['splitter'] - split_strategy=split_dict['split_strategy'] - data_dir=os.path.dirname(os.path.realpath(dataset_key)) - split=[file for file in os.listdir(data_dir) if split_uuid in file] - split_file=os.path.join(data_dir, split[0]) + splitter = split_dict['splitter'] + split_strategy = split_dict['split_strategy'] + + data_dir = os.path.dirname(os.path.realpath(original_dataset_key)) + split = [file for file in os.listdir(data_dir) if split_uuid in file] + split_file = os.path.join(data_dir, split[0]) + split_subsets = ['train', 'valid', 'test'] - split=pd.read_csv(split_file) - split=split.rename(columns={'cmpd_id':dataset_dict['id_col']}) - - # merge - df=df.merge(split, how='left') - - # get other values - response_cols=dataset_dict['response_cols'] - + split = pd.read_csv(split_file) + split = split.rename(columns={'cmpd_id': id_col}) + + # merge split labels + if id_col in df.columns: + df[id_col] = df[id_col].astype(str) + if id_col in split.columns: + split[id_col] = split[id_col].astype(str) + + df = df.merge(split, how='left', on=id_col) + # run predictions - pred_df=pfm.predict_from_model_file(model_path, df, id_col=dataset_dict['id_col'], smiles_col=dataset_dict['smiles_col'], - response_col=response_cols, is_featurized=is_featurized, AD_method=AD_method, dont_standardize=True) - + pred_df = pfm.predict_from_model_file( + model_path, + df, + id_col=id_col, + smiles_col=smiles_col, + response_col=response_cols, + is_featurized=is_featurized, + AD_method=AD_method, + dont_standardize=True + ) + # plot sns.set_context('notebook') nss = len(split_subsets) - fig, axes = plt.subplots(len(response_cols), nss, figsize=(plot_size*nss, plot_size*len(response_cols))) - if error_bars and config['model_parameters']['uncertainty']: - suptitle = f"{dataset_name} {splitter} {split_strategy} split {model_type} model on {features_label}, predicted vs actual values with uncertainty" + fig, axes = plt.subplots(len(response_cols), nss, figsize=(plot_size * nss, plot_size * len(response_cols))) + + if error_bars and config['model_parameters'].get('uncertainty'): + suptitle = ( + f"{dataset_name} {splitter} {split_strategy} split {model_type} model on {features_label}, " + f"predicted vs actual values with uncertainty" + ) else: - error_bars=False - suptitle = f"{dataset_name} {splitter} {split_strategy} split {model_type} model on {features_label}, predicted vs actual values" + error_bars = False + suptitle = ( + f"{dataset_name} {splitter} {split_strategy} split {model_type} model on {features_label}, " + f"predicted vs actual values" + ) + fig.suptitle(suptitle, y=0.95) axes = axes.flatten() - for i,resp in enumerate(response_cols): + + for i, resp in enumerate(response_cols): actual_col = f'{resp}_actual' pred_col = f'{resp}_pred' task_pred_df = pred_df[pred_df[actual_col].notna() & pred_df[pred_col].notna()] + y_actual = task_pred_df[actual_col].values y_pred = task_pred_df[pred_col].values + if error_bars: std_col = f'{resp}_std' y_std = task_pred_df[std_col].values else: - std_col=None + std_col = None y_std = 0 - ymin = min(min(y_actual), min(y_pred), min(y_pred-y_std)) - ymax = max(max(y_actual), max(y_pred), max(y_pred+y_std)) + + ymin = min(min(y_actual), min(y_pred), min(y_pred - y_std)) + ymax = max(max(y_actual), max(y_pred), max(y_pred + y_std)) + for j, subset in enumerate(split_subsets): - ax = axes[nss*i + j] - tmp = task_pred_df[task_pred_df.subset==subset] + ax = axes[nss * i + j] + tmp = task_pred_df[task_pred_df.subset == subset] r2 = metrics.r2_score(tmp[actual_col].values, tmp[pred_col].values) + ax.set_xlabel(f"Actual {resp}") ax.set_ylabel(f"Predicted {resp}") - # Set subplot title + if j == 0: subtitle = f"{resp} {subset}, {score_type_label['r2']} = {r2:.3f}" else: subtitle = f"{subset}, {score_type_label['r2']} = {r2:.3f}" - ax=plot_pred_vs_actual_from_df(tmp, actual_col=actual_col, pred_col=pred_col, std_col = std_col, label=subtitle, threshold=threshold, ax=ax) - # Force axes to have same scale for all subsets for same task (but not different tasks!) - # wont work unless applied after the graph is created + + ax = plot_pred_vs_actual_from_df( + tmp, + actual_col=actual_col, + pred_col=pred_col, + std_col=std_col, + label=subtitle, + threshold=threshold, + ax=ax + ) ax.set_xlim(ymin, ymax) ax.set_ylim(ymin, ymax) + return fig #------------------------------------------------------------------------------------------------------------------------ def plot_perf_vs_epoch(MP, plot_size=7, pdf_dir=None): @@ -1259,3 +1323,100 @@ def plot_umap_train_set_neighbors(MP, num_neighbors=20, min_dist=0.1, if pdf_dir is not None: pdf.close() MP.log.info("Wrote plot to %s" % pdf_path) + +#------------------------------------------------------------------------------------------------------------------------ +def merge_response_cols_from_original( + feat_df: pd.DataFrame, + orig_df: pd.DataFrame, + *, + id_col: str, + response_cols: list, + max_missing_frac: float = 0.01, + error_on_extra_feat_ids: bool = False, + coerce_id_to_str: bool = True, + sample_n: int = 20, +) -> pd.DataFrame: + """ + Sanity-check compound alignment between a featurized dataframe and the original dataframe, + then merge missing response columns from the original into the featurized dataframe. + + Expected behavior: + - Some original compounds may be missing in feat_df due to featurization failures. + - This missing fraction must be <= max_missing_frac, otherwise raise. + - If feat_df contains IDs not found in orig_df, warn or raise (configurable). + + Returns: + A copy of feat_df with missing response columns merged in (left join on id_col). + """ + if id_col not in feat_df.columns: + raise KeyError(f"Featurized dataframe is missing id_col '{id_col}'.") + if id_col not in orig_df.columns: + raise KeyError(f"Original dataframe is missing id_col '{id_col}'.") + + feat = feat_df.copy() + orig = orig_df.copy() + + if coerce_id_to_str: + feat[id_col] = feat[id_col].astype(str) + orig[id_col] = orig[id_col].astype(str) + + feat_ids = pd.Series(feat[id_col]).dropna() + orig_ids = pd.Series(orig[id_col]).dropna() + + n_feat_unique = feat_ids.nunique() + n_orig_unique = orig_ids.nunique() + + feat_set = set(feat_ids.unique()) + orig_set = set(orig_ids.unique()) + + missing_in_feat = orig_set - feat_set + extra_in_feat = feat_set - orig_set + + missing_frac = len(missing_in_feat) / len(orig_set) + if missing_frac > max_missing_frac: + sample = list(sorted(missing_in_feat))[:sample_n] + raise ValueError( + f"Featurized dataframe appears incomplete relative to original: " + f"{len(missing_in_feat)}/{len(orig_set)} unique compounds missing " + f"({missing_frac:.2%}), exceeds allowed {max_missing_frac:.2%}. " + f"Examples: {sample}" + ) + + if len(extra_in_feat) > 0: + sample = list(sorted(extra_in_feat))[:sample_n] + msg = ( + f"Featurized dataframe contains {len(extra_in_feat)} unique compound IDs not found in original. " + f"This may indicate mismatched files or inconsistent '{id_col}' values. " + f"Examples: {sample}" + ) + if error_on_extra_feat_ids: + raise ValueError(msg) + warnings.warn(msg) + + # Duplicate warnings (merge safety) + dup_feat = len(feat) - n_feat_unique + dup_orig = len(orig) - n_orig_unique + if dup_feat > 0: + warnings.warn( + f"Featurized dataframe has {dup_feat} duplicate '{id_col}' rows " + f"({len(feat)} rows, {n_feat_unique} unique IDs)." + ) + if dup_orig > 0: + warnings.warn( + f"Original dataframe has {dup_orig} duplicate '{id_col}' rows " + f"({len(orig)} rows, {n_orig_unique} unique IDs)." + ) + + # Merge in only missing response columns (do not overwrite existing) + available_responses = [c for c in response_cols if c in orig.columns] + if not available_responses: + return feat + + missing_response_cols = [c for c in available_responses if c not in feat.columns] + if not missing_response_cols: + return feat + + orig_subset = orig[[id_col] + missing_response_cols].drop_duplicates(subset=[id_col]) + merged = feat.merge(orig_subset, how='left', on=id_col) + + return merged diff --git a/atomsci/ddm/pipeline/transformations.py b/atomsci/ddm/pipeline/transformations.py index 7f42f003..a81e4403 100644 --- a/atomsci/ddm/pipeline/transformations.py +++ b/atomsci/ddm/pipeline/transformations.py @@ -5,11 +5,11 @@ import logging import numpy as np -import umap +import pandas as pd from deepchem.trans.transformers import Transformer, NormalizationTransformer, BalancingTransformer -from sklearn.impute import SimpleImputer -from sklearn.preprocessing import RobustScaler + +import sklearn.utils as sku logging.basicConfig(format='%(asctime)-15s %(message)s') log = logging.getLogger('ATOM') @@ -80,18 +80,12 @@ def create_feature_transformers(params, featurization, train_dset): Returns: (list of DeepChem transformer objects): list of transformers for the feature matrix """ - if params.feature_transform_type == 'umap': - # Map feature vectors using UMAP for dimension reduction - log.warning("UMAP feature transformation is deprecated and will be removed in a future release.") - if params.split_strategy == 'k_fold_cv': - log.warning("Warning: UMAP transformation may produce misleading results when used with K-fold split strategy.") - transformers_x = [UMAPTransformer(params, train_dset)] - elif params.transformers: + if params.transformers: # TODO: Transformers on responses and features should be controlled only by parameters # response_transform_type and feature_transform_type, rather than params.transformers. # Scale and center feature matrix if featurization type calls for it - transformers_x = featurization.create_feature_transformer(train_dset) + transformers_x = featurization.create_feature_transformer(train_dset, params) else: transformers_x = [] @@ -132,16 +126,21 @@ def get_transformer_specific_metadata(params): meta_dict (dict): Nested dictionary of parameters and values for each currently active transformer. """ - meta_dict = {} - if params.feature_transform_type == 'umap': - umap_dict = dict( - umap_dim = params.umap_dim, - umap_metric = params.umap_metric, - umap_targ_wt = params.umap_targ_wt, - umap_neighbors = params.umap_neighbors, - umap_min_dist = params.umap_min_dist ) - meta_dict['umap_specific'] = umap_dict - return meta_dict + meta_dict = dict( + robustscaler_with_centering = params.robustscaler_with_centering, + robustscaler_with_scaling = params.robustscaler_with_scaling, + robustscaler_quartile_range = params.robustscaler_quartile_range, + robustscaler_unit_variance = params.robustscaler_unit_variance, + powertransformer_method = params.powertransformer_method, + powertransformer_standardize = params.powertransformer_standardize, + imputer_strategy = params.imputer_strategy) + + # this parameter is set only if transformations have been loaded + # from an outside source + if hasattr(params, 'transformer_dataset_key_configs'): + meta_dict['transformer_dataset_key_configs'] = params.transformer_dataset_key_configs + + return dict(transformer_specific=meta_dict) # **************************************************************************************** def get_transformer_keys(params): @@ -207,62 +206,79 @@ def get_all_training_datasets(model_dataset): # **************************************************************************************** -class UMAPTransformer(Transformer): - """Dimension reduction transformations using the UMAP algorithm. - - DEPRECATED: Will be removed in a future release. +def zero_out_inf_nan(d): + """Return a copy of d with NaN and +/-Inf replaced by 0. + + Supports numpy arrays and pandas Series/DataFrame. + """ + # Handle pandas structures + if isinstance(d, (pd.Series, pd.DataFrame)): + arr = d.values.copy() + mask = ~np.isfinite(arr) + if mask.any(): + arr[mask] = 0.0 + if isinstance(d, pd.Series): + return pd.Series(arr, index=d.index, name=d.name) + else: + return pd.DataFrame(arr, index=d.index, columns=d.columns) + else: + # Fallback to numpy array + arr = np.array(d, copy=True) + mask = ~np.isfinite(arr) + if mask.any(): + arr[mask] = 0.0 + return arr - Attributes: - mapper (UMAP) : UMAP transformer - scaler (RobustScaler): Centering/scaling transformer +class SklearnPipelineWrapper(Transformer): + """ + This wraps a given sklearn transformer and converts it to a DeepChem style transformer """ - def __init__(self, params, dataset): - """Initializes a UMAPTransformer object. + def __init__(self, dataset, sklearn_pipeline, + transform_X=False, transform_y=False, transform_w=False): - Args: - params (Namespace): Contains parameters used to instantiate the transformer. + self.transform_X = transform_X + self.transform_y = transform_y + self.transform_w = transform_w - dataset (Dataset): Dataset used to "train" the projection mapping. - """ + assert (self.transform_X ^ self.transform_y) ^ self.transform_w, \ + "This transformer can operate on only one of X, y, or w." - # TODO: decide whether to make n_epochs a parameter - #default_n_epochs = None - default_n_epochs = 500 + self.sklearn_pipeline = sklearn_pipeline - if params.prediction_type == 'classification': - target_metric = 'categorical' + if self.transform_X: + data = dataset.X + elif self.transform_y: + data = dataset.y else: - target_metric = 'l2' - self.scaler = RobustScaler() - # Use SimpleImputer to replace missing values (NaNs) with means for each column - self.imputer = SimpleImputer() - scaled_X = self.scaler.fit_transform(self.imputer.fit_transform(dataset.X)) - self.mapper = umap.UMAP(n_neighbors=params.umap_neighbors, - n_components=params.umap_dim, - metric=params.umap_metric, - target_metric=target_metric, - target_weight=params.umap_targ_wt, - min_dist=params.umap_min_dist, - n_epochs=default_n_epochs) - # TODO: How to deal with multitask data? - self.mapper.fit(scaled_X, y=dataset.y.flatten()) - - # **************************************************************************************** + data = dataset.w + + # Fit the sklearn pipeline + try: + sku.assert_all_finite(data) + except ValueError: + # data contains inf or nan values + log.warning("SklearnPipelineWrapper: data contains NaN or Inf; replacing with zeros") + data = zero_out_inf_nan(data) + + self.sklearn_pipeline.fit(data) + def transform(self, dataset, parallel=False): - return super(UMAPTransformer, self).transform(dataset, parallel=parallel) + return dataset.transform(self) - # **************************************************************************************** def transform_array(self, X, y, w, ids): - X = self.mapper.transform(self.scaler.transform(self.imputer.transform(X))) - return (X, y, w, ids) + """Transform the data in a set of (X, y, w) arrays.""" + if self.transform_X: + X = self.sklearn_pipeline.transform(X) + elif self.transform_y: + y = self.sklearn_pipeline.transform(y) + else: + w = self.sklearn_pipeline.transform(w) - # **************************************************************************************** - def untransform(self, z): - """Reverses stored transformation on provided data.""" - raise NotImplementedError("Can't reverse a UMAP transformation") - # **************************************************************************************** + return (X, y, w, ids) + def untransform(self, z: np.ndarray) -> np.ndarray: + raise NotImplementedError("SklearnPipelineWrapper does not support inverse transforms") # **************************************************************************************** @@ -310,7 +326,7 @@ def transform_array(self, X, y, w, ids): X = np.nan_to_num(X * X_weight / self.X_stds) # zero out large values, especially for out of range test data X[np.abs(X) > 1e30] = 1e30 - if self.transform_y: + elif self.transform_y: if not hasattr(self, 'move_mean') or self.move_mean: y = np.nan_to_num((y - self.y_means) / self.y_stds) else: diff --git a/atomsci/ddm/test/integrative/balancing_trans/jsons/Identity_transformer.json b/atomsci/ddm/test/integrative/balancing_trans/jsons/Identity_transformer.json new file mode 100644 index 00000000..45870aef --- /dev/null +++ b/atomsci/ddm/test/integrative/balancing_trans/jsons/Identity_transformer.json @@ -0,0 +1,24 @@ +{ + "dataset_key" : "replaced", + "datastore" : "False", + "uncertainty": "False", + "splitter": "scaffold", + "split_valid_frac": "0.20", + "split_test_frac": "0.20", + "split_strategy": "train_valid_test", + "prediction_type": "classification", + "model_choice_score_type": "roc_auc", + "response_cols" : "active", + "id_col": "compound_id", + "smiles_col" : "rdkit_smiles", + "result_dir": "replaced", + "system": "LC", + "transformers": "True", + "model_type": "RF", + "featurizer": "computed_descriptors", + "descriptor_type": "mordred_filtered", + "feature_transform_type": "Identity", + "save_results": "False", + "verbose": "False", + "seed":"0" + } \ No newline at end of file diff --git a/atomsci/ddm/test/integrative/balancing_trans/jsons/PowerTransformer_transformer.json b/atomsci/ddm/test/integrative/balancing_trans/jsons/PowerTransformer_transformer.json new file mode 100644 index 00000000..851deb1d --- /dev/null +++ b/atomsci/ddm/test/integrative/balancing_trans/jsons/PowerTransformer_transformer.json @@ -0,0 +1,24 @@ +{ + "dataset_key" : "replaced", + "datastore" : "False", + "uncertainty": "False", + "splitter": "scaffold", + "split_valid_frac": "0.20", + "split_test_frac": "0.20", + "split_strategy": "train_valid_test", + "prediction_type": "classification", + "model_choice_score_type": "roc_auc", + "response_cols" : "active", + "id_col": "compound_id", + "smiles_col" : "rdkit_smiles", + "result_dir": "replaced", + "system": "LC", + "transformers": "True", + "model_type": "RF", + "featurizer": "computed_descriptors", + "descriptor_type": "mordred_filtered", + "feature_transform_type": "PowerTransformer", + "save_results": "False", + "verbose": "False", + "seed":"0" + } \ No newline at end of file diff --git a/atomsci/ddm/test/integrative/balancing_trans/jsons/RobustScaler_transformer.json b/atomsci/ddm/test/integrative/balancing_trans/jsons/RobustScaler_transformer.json new file mode 100644 index 00000000..9a171a04 --- /dev/null +++ b/atomsci/ddm/test/integrative/balancing_trans/jsons/RobustScaler_transformer.json @@ -0,0 +1,29 @@ +{ + "dataset_key" : "replaced", + "datastore" : "False", + "uncertainty": "False", + "splitter": "scaffold", + "split_valid_frac": "0.20", + "split_test_frac": "0.20", + "split_strategy": "train_valid_test", + "prediction_type": "classification", + "model_choice_score_type": "roc_auc", + "response_cols" : "active", + "id_col": "compound_id", + "smiles_col" : "rdkit_smiles", + "result_dir": "replaced", + "system": "LC", + "transformers": "True", + "model_type": "RF", + "featurizer": "computed_descriptors", + "descriptor_type": "mordred_filtered", + "feature_transform_type": "RobustScaler", + "robustscaler_with_center": "True", + "robustscaler_with_scaling": "True", + "robustscaler_quartile_range": "30.0,80.0", + "robustscaler_unit_variance": "True", + "imputer_strategy": "median", + "save_results": "False", + "verbose": "False", + "seed":"0" + } \ No newline at end of file diff --git a/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py b/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py index b1b156c7..226bfe57 100644 --- a/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py +++ b/atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py @@ -1,12 +1,21 @@ import tempfile +import pytest +import re import atomsci.ddm.pipeline.parameter_parser as parse import atomsci.ddm.pipeline.model_pipeline as mp import atomsci.ddm.pipeline.transformations as trans +import atomsci.ddm.pipeline.featurization as feat +import atomsci.ddm.pipeline.compare_models as cm +import atomsci.ddm.utils.model_file_reader as mfr import numpy as np import os import json +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import RobustScaler, PowerTransformer +from sklearn.pipeline import Pipeline + import sys sys.path.append(os.path.join(os.path.dirname(__file__), '..')) import make_test_datasets @@ -361,9 +370,97 @@ def test_kfold_regression_transformers(): # transformer means should be around expected_mean np.testing.assert_array_almost_equal(transformer.y_means, expected_y_means) +def test_sklearn_pipelines(): + """ + Test the balancing transformer to ensure that it correctly adjusts weights for imbalanced datasets. + """ + dset_key = make_relative_to_file('../../test_datasets/MRP3_dataset.csv') + res_dir = tempfile.mkdtemp() + + robustscaler_params = read_params( + make_relative_to_file('jsons/RobustScaler_transformer.json'), + dset_key, + res_dir + ) + + robustscaler_pipe = make_pipeline(robustscaler_params) + transformers_x = robustscaler_pipe.model_wrapper.transformers_x + assert len(transformers_x[0])==1 + assert isinstance(transformers_x[0][0], trans.SklearnPipelineWrapper) + assert isinstance(transformers_x[0][0].sklearn_pipeline, Pipeline) + scaler = transformers_x[0][0].sklearn_pipeline.named_steps['RobustScaler'] + assert isinstance(scaler, RobustScaler) + imputer = transformers_x[0][0].sklearn_pipeline.named_steps['SimpleImputer'] + assert isinstance(imputer, SimpleImputer) + assert imputer.strategy == 'median' + + tar_path = cm.get_filesystem_perf_results(res_dir)['model_path'].values[0] + reader = mfr.ModelFileReader(tar_path) + + assert reader.get_robustscaler_with_centering() + assert reader.get_robustscaler_with_scaling() + assert reader.get_robustscaler_quartile_range() == [30.0, 80.0] + assert reader.get_robustscaler_unit_variance() + assert reader.get_imputer_strategy() == 'median' + + res_dir = tempfile.mkdtemp() + powertransformer_params = read_params( + make_relative_to_file('jsons/PowerTransformer_transformer.json'), + dset_key, + res_dir + ) + + powertransformer_pipe = make_pipeline(powertransformer_params) + transformers_x = powertransformer_pipe.model_wrapper.transformers_x + assert len(transformers_x[0])==1 + assert isinstance(transformers_x[0][0], trans.SklearnPipelineWrapper) + assert isinstance(transformers_x[0][0].sklearn_pipeline, Pipeline) + scaler = transformers_x[0][0].sklearn_pipeline.named_steps['PowerTransformer'] + assert isinstance(scaler, PowerTransformer) + + tar_path = cm.get_filesystem_perf_results(res_dir)['model_path'].values[0] + reader = mfr.ModelFileReader(tar_path) + + assert reader.get_powertransformer_method() == 'yeo-johnson' + assert reader.get_powertransformer_standardize() + + res_dir = tempfile.mkdtemp() + identity_params = read_params( + make_relative_to_file('jsons/Identity_transformer.json'), + dset_key, + res_dir + ) + + # borrow the pipeline from PowerTransforer to test + # the Identity transformer since MRP3 contains nans in the dataset + # So it cannot train. Since it cannot train it will never make a model_wrapper + # which is what makes the feature transformers. + identity_pparams = parse.wrapper(identity_params) + transformers_x = powertransformer_pipe.model_wrapper.featurization.create_feature_transformer( + dataset=None, + params=identity_pparams + ) + assert len(transformers_x)==0 + + res_dir = tempfile.mkdtemp() + with pytest.raises( + ValueError, + match=re.escape( + "feature_transform_type must be normalization, RobustScaler, " + "PowerTransformer, or Identity. Got NOTREALTRANSFORMER", + ), + ): + # Test that an unrecognized transformer raises the correct error + identity_pparams.feature_transform_type = 'NOTREALTRANSFORMER' + transformers_x = powertransformer_pipe.model_wrapper.featurization.create_feature_transformer( + dataset=None, + params=identity_pparams + ) + if __name__ == '__main__': - test_kfold_regression_transformers() - test_kfold_transformers() - test_all_transformers() - test_balancing_transformer() \ No newline at end of file + test_sklearn_pipelines() + #test_kfold_regression_transformers() + #test_kfold_transformers() + #test_all_transformers() + #test_balancing_transformer() \ No newline at end of file diff --git a/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_mordred_filtered_scaled.json b/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_mordred_filtered_scaled.json new file mode 100644 index 00000000..77c7294e --- /dev/null +++ b/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_mordred_filtered_scaled.json @@ -0,0 +1,36 @@ +{ + "comment": "System", + "comment": "----------------------------------------", + "system": "LC", + "datastore": "False", + "save_results": "False", + "data_owner": "username", + + "comment": "Input file", + "comment": "----------------------------------------", + "comment": "Note: dataset_key must be a path/file name: E.G. ./dataset.csv", + "dataset_key": "./delaney-processed_curated_fit.csv", + "id_col": "Compound ID", + "smiles_col": "smiles", + + "comment": "Prediction Type", + "comment": "----------------------------------------", + "response_cols": "measured log solubility in mols per litre_class", + "prediction_type": "classification", + + "comment": "Features", + "comment": "----------------------------------------", + "featurizer": "computed_descriptors", + + "comment": "Descriptors", + "comment": "----------------------------------------", + "descriptor_type": "mordred_filtered_scaled", + + "comment": "Model", + "comment": "----------------------------------------", + "model_type": "xgboost", + + "comment": "Results", + "comment": "----------------------------------------", + "result_dir": "result" +} diff --git a/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_rdkit_scaled.json b/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_rdkit_scaled.json new file mode 100644 index 00000000..7b1b731c --- /dev/null +++ b/atomsci/ddm/test/integrative/delaney_Panel/jsons/class_config_delaney_fit_XGB_rdkit_scaled.json @@ -0,0 +1,36 @@ +{ + "comment": "System", + "comment": "----------------------------------------", + "system": "LC", + "datastore": "False", + "save_results": "False", + "data_owner": "username", + + "comment": "Input file", + "comment": "----------------------------------------", + "comment": "Note: dataset_key must be a path/file name: E.G. ./dataset.csv", + "dataset_key": "./delaney-processed_curated_fit.csv", + "id_col": "Compound ID", + "smiles_col": "smiles", + + "comment": "Prediction Type", + "comment": "----------------------------------------", + "response_cols": "measured log solubility in mols per litre_class", + "prediction_type": "classification", + + "comment": "Features", + "comment": "----------------------------------------", + "featurizer": "computed_descriptors", + + "comment": "Descriptors", + "comment": "----------------------------------------", + "descriptor_type": "rdkit_scaled", + + "comment": "Model", + "comment": "----------------------------------------", + "model_type": "xgboost", + + "comment": "Results", + "comment": "----------------------------------------", + "result_dir": "result" +} diff --git a/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_powertransformer.json b/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_powertransformer.json new file mode 100644 index 00000000..ec2a8592 --- /dev/null +++ b/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_powertransformer.json @@ -0,0 +1,57 @@ +{ + "comment": "System", + "comment": "----------------------------------------", + "system": "LC", + "datastore": "False", + "save_results": "False", + "data_owner": "username", + + "comment": "Input file", + "comment": "----------------------------------------", + "comment": "Note: dataset_key must be a path/file name: E.G. ./dataset.csv", + "dataset_key": "./delaney-processed_curated_fit.csv", + "id_col": "Compound ID", + "smiles_col": "smiles", + + "comment": "Prediction Type", + "comment": "----------------------------------------", + "response_cols": ["measured log solubility in mols per litre_class", + "measured log solubility in mols per litre_class2"], + "num_model_tasks": 2, + "prediction_type": "classification", + + "comment": "Features", + "comment": "----------------------------------------", + "featurizer": "computed_descriptors", + + "comment": "Descriptors", + "comment": "----------------------------------------", + "descriptor_type": "mordred_filtered", + + "comment": "Splitting", + "comment": "----------------------------------------", + "base_spliter": "random", + "spliter": "random", + + "comment": "Model", + "comment": "----------------------------------------", + "model_type": "NN", + "dropout": ".01,.01,.01", + "layer_sizes": "64,64,64", + "learning_rate": "0.007", + "max_epochs": "50", + + "comment": "Transformers", + "comment": "----------------------------------------", + "comment": "Feature transformer type", + "feature_transformer_type": "PowerTransformer", + + "comment": "Training", + "comment": "----------------------------------------", + "comment": "This regulates how long to train the model", + "early_stopping_patience": "50", + + "comment": "Results", + "comment": "----------------------------------------", + "result_dir": "result" +} diff --git a/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_robustscaler.json b/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_robustscaler.json new file mode 100644 index 00000000..6f73e363 --- /dev/null +++ b/atomsci/ddm/test/integrative/delaney_Panel/jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_robustscaler.json @@ -0,0 +1,57 @@ +{ + "comment": "System", + "comment": "----------------------------------------", + "system": "LC", + "datastore": "False", + "save_results": "False", + "data_owner": "username", + + "comment": "Input file", + "comment": "----------------------------------------", + "comment": "Note: dataset_key must be a path/file name: E.G. ./dataset.csv", + "dataset_key": "./delaney-processed_curated_fit.csv", + "id_col": "Compound ID", + "smiles_col": "smiles", + + "comment": "Prediction Type", + "comment": "----------------------------------------", + "response_cols": ["measured log solubility in mols per litre_class", + "measured log solubility in mols per litre_class2"], + "num_model_tasks": 2, + "prediction_type": "classification", + + "comment": "Features", + "comment": "----------------------------------------", + "featurizer": "computed_descriptors", + + "comment": "Descriptors", + "comment": "----------------------------------------", + "descriptor_type": "mordred_filtered", + + "comment": "Splitting", + "comment": "----------------------------------------", + "base_spliter": "random", + "spliter": "random", + + "comment": "Model", + "comment": "----------------------------------------", + "model_type": "NN", + "dropout": ".01,.01,.01", + "layer_sizes": "64,64,64", + "learning_rate": "0.007", + "max_epochs": "50", + + "comment": "Transformers", + "comment": "----------------------------------------", + "comment": "Feature transformer type", + "feature_transformer_type": "RobustScaler", + + "comment": "Training", + "comment": "----------------------------------------", + "comment": "This regulates how long to train the model", + "early_stopping_patience": "50", + + "comment": "Results", + "comment": "----------------------------------------", + "result_dir": "result" +} diff --git a/atomsci/ddm/test/integrative/delaney_Panel/test_delaney_panel.py b/atomsci/ddm/test/integrative/delaney_Panel/test_delaney_panel.py index f981b561..49a60f94 100644 --- a/atomsci/ddm/test/integrative/delaney_Panel/test_delaney_panel.py +++ b/atomsci/ddm/test/integrative/delaney_Panel/test_delaney_panel.py @@ -299,6 +299,14 @@ def test_class_config_delaney_fit_XGB_mordred_filtered(): init() train_and_predict('jsons/class_config_delaney_fit_XGB_mordred_filtered.json') # breaks because labels aren't numbers +def test_class_config_delaney_fit_XGB_rdkit_scaled(): + init() + train_and_predict('jsons/class_config_delaney_fit_XGB_rdkit_scaled.json') # fine + +def test_class_config_delaney_fit_XGB_mordred_filtered_scaled(): + init() + train_and_predict('jsons/class_config_delaney_fit_XGB_mordred_filtered_scaled.json') # fine + def test_class_config_delaney_fit_NN_ecfp(): init() train_and_predict('jsons/class_config_delaney_fit_NN_ecfp.json') # only works for class @@ -307,6 +315,14 @@ def test_multi_class_random_config_delaney_fit_NN_mordred_filtered(): init() train_and_predict('jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered.json') # crashes during run +def test_multi_class_random_config_delaney_fit_NN_mordred_filtered_robustscaler(): + init() + train_and_predict('jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_robustscaler.json') # crashes during run + +def test_multi_class_random_config_delaney_fit_NN_mordred_filtered_powertransformer(): + init() + train_and_predict('jsons/multi_class_random_config_delaney_fit_NN_mordred_filtered_powertransformer.json') # crashes during run + def test_multi_class_config_delaney_fit_NN_graphconv(): init() train_and_predict('jsons/multi_class_config_delaney_fit_NN_graphconv.json') # fine @@ -349,5 +365,6 @@ def test_class_config_H1_fit_NN_moe(): train_and_predict('jsons/class_config_H1_fit_NN_moe.json', prefix='H1') if __name__ == '__main__': - test_reg_kfold_config_delaney_fit_NN_graphconv() + test_class_config_delaney_fit_XGB_rdkit_scaled() + test_class_config_delaney_fit_XGB_mordred_filtered_scaled() #pass diff --git a/atomsci/ddm/test/integrative/import_transformer/data/H1_std_short.csv b/atomsci/ddm/test/integrative/import_transformer/data/H1_std_short.csv new file mode 100644 index 00000000..4929e0f8 --- /dev/null +++ b/atomsci/ddm/test/integrative/import_transformer/data/H1_std_short.csv @@ -0,0 +1,241 @@ +base_rdkit_smiles,compound_id,NofA,pKi_mean,pKi_std,active 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/dev/null +++ b/atomsci/ddm/test/integrative/import_transformer/data/delaney-processed_curated_fit_short.csv @@ -0,0 +1,201 @@ +Unnamed: 0,Compound ID,ESOL predicted log solubility in mols per litre,Minimum Degree,Molecular Weight,Number of H-Bond Donors,Number of Rings,Number of Rotatable Bonds,Polar Surface Area,measured log solubility in mols per litre,smiles,rdkit_smiles,inchi_key,compound_id,VALUE_NUM_mean,VALUE_NUM_std,Perc_Var,Remove_BadDuplicate,measured log solubility in mols per litre_class,measured log solubility in mols per litre2,measured log solubility in mols per litre_class2 +117,Anilofos,-5.106,1,367.86,0,1,7,38.77,-4.432,COP(=S)(OC)SCC(=O)N(C(C)C)c1ccc(Cl)cc1,COP(=S)(OC)SCC(=O)N(c1ccc(Cl)cc1)C(C)C,NXQDBZGWYSEGFL-UHFFFAOYSA-N,NXQDBZGWYSEGFL-UHFFFAOYSA-N,-4.432,,-0.0,0,0.0,-4.432,0.0 +118,Hexylbenzene ,-4.22,1,162.276,0,1,5,0.0,-5.21,CCCCCCc1ccccc1,CCCCCCc1ccccc1,LTEQMZWBSYACLV-UHFFFAOYSA-N,LTEQMZWBSYACLV-UHFFFAOYSA-N,-5.21,,-0.0,0,0.0,-5.21,0.0 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+227,"2-Methyl-1,3-Butadiene ",-1.714,1,68.11900000000001,0,0,1,0.0,-2.03,CC(=C)C=C,C=CC(=C)C,RRHGJUQNOFWUDK-UHFFFAOYSA-N,RRHGJUQNOFWUDK-UHFFFAOYSA-N,-2.03,,-0.0,0,1.0,-2.03,1.0 +228,linalool,-2.399,1,154.253,1,0,4,20.23,-1.99,CC(C)=CCCC(O)(C)C=C,C=CC(C)(O)CCC=C(C)C,CDOSHBSSFJOMGT-UHFFFAOYSA-N,CDOSHBSSFJOMGT-UHFFFAOYSA-N,-1.99,,-0.0,0,1.0,-1.99,1.0 +229,Fenthion,-4.265,1,278.335,0,1,5,27.69,-4.57,COP(=S)(OC)Oc1ccc(SC)c(C)c1,COP(=S)(OC)Oc1ccc(SC)c(C)c1,PNVJTZOFSHSLTO-UHFFFAOYSA-N,PNVJTZOFSHSLTO-UHFFFAOYSA-N,-4.57,,-0.0,0,0.0,-4.57,0.0 +230,Cyclohexanol ,-1.261,1,100.161,1,1,0,20.23,-0.44,OC1CCCCC1,OC1CCCCC1,HPXRVTGHNJAIIH-UHFFFAOYSA-N,HPXRVTGHNJAIIH-UHFFFAOYSA-N,-0.44,,-0.0,0,1.0,-0.44,1.0 +231,5-Allyl-5-methylbarbital,-1.013,1,182.179,2,1,2,75.27000000000001,-1.16,O=C1NC(=O)NC(=O)C1(C)CC=C,C=CCC1(C)C(=O)NC(=O)NC1=O,WMSQREUDUHHDDC-UHFFFAOYSA-N,WMSQREUDUHHDDC-UHFFFAOYSA-N,-1.16,,-0.0,0,1.0,-1.16,1.0 +232,Epiandrosterone,-3.882,1,290.447,1,4,0,37.3,-4.16,CC34CCC1C(CCC2CC(O)CCC12C)C3CCC4=O,CC12CCC3C(CCC4CC(O)CCC43C)C1CCC2=O,QGXBDMJGAMFCBF-UHFFFAOYSA-N,QGXBDMJGAMFCBF-UHFFFAOYSA-N,-4.281000000000001,0.1711198410471441,-2.826442419995338,0,0.0,-4.16,0.0 +233,mannitol,0.647,1,182.172,6,0,5,121.38,0.06,OCC(O)C(O)C(O)C(O)CO ,OCC(O)C(O)C(O)C(O)CO,FBPFZTCFMRRESA-UHFFFAOYSA-N,FBPFZTCFMRRESA-UHFFFAOYSA-N,0.5750000000000001,0.728319984622144,89.56521739130436,0,1.0,0.06,1.0 +234,4-Methylbiphenyl,-4.424,1,168.239,0,2,1,0.0,-4.62,Cc1ccc(cc1)c2ccccc2,Cc1ccc(-c2ccccc2)cc1,ZZLCFHIKESPLTH-UHFFFAOYSA-N,ZZLCFHIKESPLTH-UHFFFAOYSA-N,-4.62,,-0.0,0,0.0,-4.62,0.0 +235,Atrazine,-3.069,1,215.688,2,1,4,62.73,-3.85,CCNc1nc(Cl)nc(NC(C)C)n1,CCNc1nc(Cl)nc(NC(C)C)n1,MXWJVTOOROXGIU-UHFFFAOYSA-N,MXWJVTOOROXGIU-UHFFFAOYSA-N,-3.85,,-0.0,0,0.0,-3.85,0.0 +236,Phenylthiourea,-1.7009999999999998,1,152.22199999999998,2,1,1,38.05,-1.77,NC(=S)Nc1ccccc1,NC(=S)Nc1ccccc1,FULZLIGZKMKICU-UHFFFAOYSA-N,FULZLIGZKMKICU-UHFFFAOYSA-N,-1.77,,-0.0,0,1.0,-1.77,1.0 +237,4-Heptanone,-1.62,1,114.188,0,0,4,17.07,-1.3,CCCC(=O)CCC,CCCC(=O)CCC,HCFAJYNVAYBARA-UHFFFAOYSA-N,HCFAJYNVAYBARA-UHFFFAOYSA-N,-1.3,,-0.0,0,1.0,-1.3,1.0 +238,"3,3-Dimethyl-2-butanone",-1.25,1,100.161,0,0,0,17.07,-0.72,CC(=O)C(C)(C)C,CC(=O)C(C)(C)C,PJGSXYOJTGTZAV-UHFFFAOYSA-N,PJGSXYOJTGTZAV-UHFFFAOYSA-N,-0.72,,-0.0,0,1.0,-0.72,1.0 +239,4-Chlorophenol ,-2.761,1,128.558,1,1,0,20.23,-0.7,Oc1ccc(Cl)cc1,Oc1ccc(Cl)cc1,WXNZTHHGJRFXKQ-UHFFFAOYSA-N,WXNZTHHGJRFXKQ-UHFFFAOYSA-N,-0.7,,-0.0,0,1.0,-0.7,1.0 +240,Cyclohexanone,-0.996,1,98.145,0,1,0,17.07,-0.6,O=C1CCCCC1,O=C1CCCCC1,JHIVVAPYMSGYDF-UHFFFAOYSA-N,JHIVVAPYMSGYDF-UHFFFAOYSA-N,-0.6,,-0.0,0,1.0,-0.6,1.0 +241,m-Methylaniline,-1.954,1,107.156,1,1,0,26.02,-0.85,Cc1cccc(N)c1,Cc1cccc(N)c1,JJYPMNFTHPTTDI-UHFFFAOYSA-N,JJYPMNFTHPTTDI-UHFFFAOYSA-N,-0.85,,-0.0,0,1.0,-0.85,1.0 +242,Trichloroacetonitrile,-2.019,1,144.388,0,0,0,23.79,-2.168,ClC(Cl)(Cl)C#N,N#CC(Cl)(Cl)Cl,DRUIESSIVFYOMK-UHFFFAOYSA-N,DRUIESSIVFYOMK-UHFFFAOYSA-N,-2.168,,-0.0,0,1.0,-2.168,1.0 +243,norflurazon,-4.029,1,303.67100000000005,1,2,2,46.92,-4.046,CNc2cnn(c1cccc(c1)C(F)(F)F)c(=O)c2Cl,CNc1cnn(-c2cccc(C(F)(F)F)c2)c(=O)c1Cl,NVGOPFQZYCNLDU-UHFFFAOYSA-N,NVGOPFQZYCNLDU-UHFFFAOYSA-N,-4.046,,-0.0,0,0.0,-4.046,0.0 +244,2-Decanone,-2.617,1,156.269,0,0,7,17.07,-3.3,CCCCCCCCC(=O)C,CCCCCCCCC(C)=O,ZAJNGDIORYACQU-UHFFFAOYSA-N,ZAJNGDIORYACQU-UHFFFAOYSA-N,-3.3,,-0.0,0,0.0,-3.3,0.0 +245,Ipazine,-3.497,1,243.742,1,1,5,53.940000000000005,-3.785,CCN(CC)c1nc(Cl)nc(NC(C)C)n1,CCN(CC)c1nc(Cl)nc(NC(C)C)n1,OWYWGLHRNBIFJP-UHFFFAOYSA-N,OWYWGLHRNBIFJP-UHFFFAOYSA-N,-3.785,,-0.0,0,0.0,-3.785,0.0 +246,Benzocaine,-2.383,1,165.19199999999998,1,1,2,52.32,-2.616,CCOC(=O)c1ccc(N)cc1,CCOC(=O)c1ccc(N)cc1,BLFLLBZGZJTVJG-UHFFFAOYSA-N,BLFLLBZGZJTVJG-UHFFFAOYSA-N,-2.616,,-0.0,0,1.0,-2.616,1.0 +247,"1,2,4-Trichlorobenzene",-4.083,1,181.449,0,1,0,0.0,-3.59,Clc1ccc(Cl)c(Cl)c1,Clc1ccc(Cl)c(Cl)c1,PBKONEOXTCPAFI-UHFFFAOYSA-N,PBKONEOXTCPAFI-UHFFFAOYSA-N,-3.59,,-0.0,0,0.0,-3.59,0.0 +248,Triazolam,-3.948,1,343.2170000000001,0,4,1,43.07,-4.09,Cc3nnc4CN=C(c1ccccc1Cl)c2cc(Cl)ccc2n34,Cc1nnc2n1-c1ccc(Cl)cc1C(c1ccccc1Cl)=NC2,JOFWLTCLBGQGBO-UHFFFAOYSA-N,JOFWLTCLBGQGBO-UHFFFAOYSA-N,-4.09,,-0.0,0,0.0,-4.09,0.0 +249,"1,2-Benzenediol",-1.635,1,110.11199999999998,2,1,0,40.46,0.62,Oc1ccccc1O,Oc1ccccc1O,YCIMNLLNPGFGHC-UHFFFAOYSA-N,YCIMNLLNPGFGHC-UHFFFAOYSA-N,0.62,,0.0,0,1.0,0.62,1.0 +250,Reverse Transcriptase inhibitor 1,-2.794,1,254.293,0,3,1,49.330000000000005,-2.62,CCN2c1ncccc1N(C)C(=O)c3cccnc23 ,CCN1c2ncccc2C(=O)N(C)c2cccnc21,SVFKQHLOBRUZGV-UHFFFAOYSA-N,SVFKQHLOBRUZGV-UHFFFAOYSA-N,-2.62,,-0.0,0,1.0,-2.62,1.0 +251,Dimethyl sulfide,-0.758,1,62.137,0,0,0,0.0,-0.45,CSC,CSC,QMMFVYPAHWMCMS-UHFFFAOYSA-N,QMMFVYPAHWMCMS-UHFFFAOYSA-N,-0.45,,-0.0,0,1.0,-0.45,1.0 +252,2-Bromotoluene,-3.667,1,171.03699999999998,0,1,0,0.0,-2.23,Cc1ccccc1Br,Cc1ccccc1Br,QSSXJPIWXQTSIX-UHFFFAOYSA-N,QSSXJPIWXQTSIX-UHFFFAOYSA-N,-2.23,,-0.0,0,1.0,-2.23,1.0 +253,O-Ethyl carbamate,-0.218,1,89.09400000000001,1,0,1,52.32,0.85,CCOC(=O)N,CCOC(N)=O,JOYRKODLDBILNP-UHFFFAOYSA-N,JOYRKODLDBILNP-UHFFFAOYSA-N,0.85,,0.0,0,1.0,0.85,1.0 +254,megestrol acetate,-4.417,1,384.5160000000002,0,4,2,60.440000000000005,-5.35,CC(=O)OC3(CCC4C2C=C(C)C1=CC(=O)CCC1(C)C2CCC34C)C(C)=O ,CC(=O)OC1(C(C)=O)CCC2C3C=C(C)C4=CC(=O)CCC4(C)C3CCC21C,RQZAXGRLVPAYTJ-UHFFFAOYSA-N,RQZAXGRLVPAYTJ-UHFFFAOYSA-N,-5.35,,-0.0,0,0.0,-5.35,0.0 +255,"2,4-Dimethyl-3-pentanol",-1.6469999999999998,1,116.20399999999998,1,0,2,20.23,-1.22,CC(C)C(O)C(C)C,CC(C)C(O)C(C)C,BAYAKMPRFGNNFW-UHFFFAOYSA-N,BAYAKMPRFGNNFW-UHFFFAOYSA-N,-1.22,,-0.0,0,1.0,-1.22,1.0 +256,Napthalene,-3.468,2,128.17399999999995,0,2,0,0.0,-3.6,c1ccc2ccccc2c1,c1ccc2ccccc2c1,UFWIBTONFRDIAS-UHFFFAOYSA-N,UFWIBTONFRDIAS-UHFFFAOYSA-N,-3.6,,-0.0,0,0.0,-3.6,0.0 +257,N-Ethylaniline,-2.389,1,121.18299999999996,1,1,2,12.03,-1.7,CCNc1ccccc1,CCNc1ccccc1,OJGMBLNIHDZDGS-UHFFFAOYSA-N,OJGMBLNIHDZDGS-UHFFFAOYSA-N,-1.7,,-0.0,0,1.0,-1.7,1.0 +258,Phenytoin,-3.057,1,252.273,2,3,2,58.2,-4.097,O=C1NC(=O)C(N1)(c2ccccc2)c3ccccc3,O=C1NC(=O)C(c2ccccc2)(c2ccccc2)N1,CXOFVDLJLONNDW-UHFFFAOYSA-N,CXOFVDLJLONNDW-UHFFFAOYSA-N,-4.097,,-0.0,0,0.0,-4.097,0.0 +259,"7,12-Dimethylbenz(a)anthracene",-6.297000000000001,1,256.348,0,4,0,0.0,-7.02,Cc1c2ccccc2c(C)c3ccc4ccccc4c13,Cc1c2ccccc2c(C)c2c1ccc1ccccc12,ARSRBNBHOADGJU-UHFFFAOYSA-N,ARSRBNBHOADGJU-UHFFFAOYSA-N,-7.02,,-0.0,0,0.0,-7.02,0.0 +260,Dialifor,-5.026,1,393.85400000000016,0,2,8,55.84,-6.34,CCOP(=S)(OCC)SC(CCl)N1C(=O)c2ccccc2C1=O,CCOP(=S)(OCC)SC(CCl)N1C(=O)c2ccccc2C1=O,MUMQYXACQUZOFP-UHFFFAOYSA-N,MUMQYXACQUZOFP-UHFFFAOYSA-N,-6.34,0.0,-0.0,0,0.0,-6.34,0.0 +261,Methoxychlor,-5.537999999999999,1,345.6529999999999,0,2,4,18.46,-6.89,COc1ccc(cc1)C(c2ccc(OC)cc2)C(Cl)(Cl)Cl,COc1ccc(C(c2ccc(OC)cc2)C(Cl)(Cl)Cl)cc1,IAKOZHOLGAGEJT-UHFFFAOYSA-N,IAKOZHOLGAGEJT-UHFFFAOYSA-N,-6.89,,-0.0,0,0.0,-6.89,0.0 +262,TEFLUBENZURON,-5.462000000000001,1,381.1120000000001,2,2,2,58.2,-7.28,Fc1cccc(F)c1C(=O)NC(=O)Nc2cc(Cl)c(F)c(Cl)c2F ,O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(F)c(Cl)c1F,CJDWRQLODFKPEL-UHFFFAOYSA-N,CJDWRQLODFKPEL-UHFFFAOYSA-N,-7.28,,-0.0,0,0.0,-7.28,0.0 +263,3-Pentanoyloxymethylphenytoin,-3.81,1,366.417,1,3,7,75.71,-4.678,O=C1N(COC(=O)CCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3,CCCCC(=O)OCN1C(=O)NC(c2ccccc2)(c2ccccc2)C1=O,XCEYNQCFXXAOBL-UHFFFAOYSA-N,XCEYNQCFXXAOBL-UHFFFAOYSA-N,-4.678,,-0.0,0,0.0,-4.678,0.0 +264,Monuron,-2.6710000000000003,1,198.653,1,1,1,32.34,-2.89,CN(C)C(=O)Nc1ccc(Cl)cc1,CN(C)C(=O)Nc1ccc(Cl)cc1,BMLIZLVNXIYGCK-UHFFFAOYSA-N,BMLIZLVNXIYGCK-UHFFFAOYSA-N,-2.89,,-0.0,0,1.0,-2.89,1.0 +265,Flutriafol,-3.569,1,301.296,1,3,4,50.94,-3.37,OC(Cn1cncn1)(c2ccc(F)cc2)c3ccccc3F,OC(Cn1cncn1)(c1ccc(F)cc1)c1ccccc1F,JWUCHKBSVLQQCO-UHFFFAOYSA-N,JWUCHKBSVLQQCO-UHFFFAOYSA-N,-3.37,,-0.0,0,0.0,-3.37,0.0 +266,triamcinolone diacetate,-3.876,1,478.51300000000026,2,4,4,127.20000000000002,-4.13,CC(=O)OCC(=O)C3(O)C(CC4C2CCC1=CC(=O)C=CC1(C)C2(F)C(O)CC34C)OC(C)=O ,CC(=O)OCC(=O)C1(O)C(OC(C)=O)CC2C3CCC4=CC(=O)C=CC4(C)C3(F)C(O)CC21C,XGMPVBXKDAHORN-UHFFFAOYSA-N,XGMPVBXKDAHORN-UHFFFAOYSA-N,-4.13,,-0.0,0,0.0,-4.13,0.0 +267,1-Bromobutane,-2.303,1,137.01999999999998,0,0,2,0.0,-2.37,CCCCBr,CCCCBr,MPPPKRYCTPRNTB-UHFFFAOYSA-N,MPPPKRYCTPRNTB-UHFFFAOYSA-N,-2.37,,-0.0,0,1.0,-2.37,1.0 +268,"1,2,4,5-Tetrabromobenzene",-6.001,1,393.698,0,1,0,0.0,-6.98,Brc1cc(Br)c(Br)cc1Br,Brc1cc(Br)c(Br)cc1Br,QCKHVNQHBOGZER-UHFFFAOYSA-N,QCKHVNQHBOGZER-UHFFFAOYSA-N,-6.98,,-0.0,0,0.0,-6.98,0.0 +269,4-Methyl-2-pentanone,-1.1840000000000002,1,100.161,0,0,2,17.07,-0.74,CC(C)CC(=O)C,CC(=O)CC(C)C,NTIZESTWPVYFNL-UHFFFAOYSA-N,NTIZESTWPVYFNL-UHFFFAOYSA-N,-0.74,,-0.0,0,1.0,-0.74,1.0 +270,cycloate,-3.35,1,215.362,0,1,3,20.31,-3.4,CCSC(=O)N(CC)C1CCCCC1 ,CCSC(=O)N(CC)C1CCCCC1,DFCAFRGABIXSDS-UHFFFAOYSA-N,DFCAFRGABIXSDS-UHFFFAOYSA-N,-3.4,,-0.0,0,0.0,-3.4,0.0 +271,4-Chloroanisole,-3.057,1,142.585,0,1,1,9.23,-2.78,COc1ccc(Cl)cc1,COc1ccc(Cl)cc1,YRGAYAGBVIXNAQ-UHFFFAOYSA-N,YRGAYAGBVIXNAQ-UHFFFAOYSA-N,-2.78,,-0.0,0,1.0,-2.78,1.0 +272,Deltamethrin,-7.44,1,505.2060000000002,0,3,6,59.32000000000001,-8.402000000000001,CC1(C)C(C=C(Br)Br)C1C(=O)OC(C#N)c2cccc(Oc3ccccc3)c2,CC1(C)C(C=C(Br)Br)C1C(=O)OC(C#N)c1cccc(Oc2ccccc2)c1,OWZREIFADZCYQD-UHFFFAOYSA-N,OWZREIFADZCYQD-UHFFFAOYSA-N,-8.402000000000001,,-0.0,0,0.0,-8.402000000000001,0.0 +273,Talbutal,-2.06,1,224.26,2,1,4,75.27000000000001,-2.016,CCC(C)C1(CC=C)C(=O)NC(=O)NC1=O,C=CCC1(C(C)CC)C(=O)NC(=O)NC1=O,BJVVMKUXKQHWJK-UHFFFAOYSA-N,BJVVMKUXKQHWJK-UHFFFAOYSA-N,-2.016,,-0.0,0,1.0,-2.016,1.0 +274,Fenitrothion,-3.845,1,277.238,0,1,5,70.83000000000001,-4.04,COP(=S)(OC)Oc1ccc(N(=O)=O)c(C)c1,COP(=S)(OC)Oc1ccc([N+](=O)[O-])c(C)c1,ZNOLGFHPUIJIMJ-UHFFFAOYSA-N,ZNOLGFHPUIJIMJ-UHFFFAOYSA-N,-4.04,,-0.0,0,0.0,-4.04,0.0 +275,1-Iodonapthalene,-4.888999999999999,1,254.07,0,2,0,0.0,-4.55,Ic1cccc2ccccc12,Ic1cccc2ccccc12,NHPPIJMARIVBGU-UHFFFAOYSA-N,NHPPIJMARIVBGU-UHFFFAOYSA-N,-4.55,,-0.0,0,0.0,-4.55,0.0 +277,Ethanethiol,-0.968,1,62.137,1,0,0,0.0,-0.6,CCS,CCS,DNJIEGIFACGWOD-UHFFFAOYSA-N,DNJIEGIFACGWOD-UHFFFAOYSA-N,-0.6,,-0.0,0,1.0,-0.6,1.0 +278,"1,1,2-Trichloroethane",-1.961,1,133.405,0,0,1,0.0,-1.48,ClCC(Cl)Cl,ClCC(Cl)Cl,UBOXGVDOUJQMTN-UHFFFAOYSA-N,UBOXGVDOUJQMTN-UHFFFAOYSA-N,-1.48,,-0.0,0,1.0,-1.48,1.0 +279,Pyrolan,-3.141,1,245.282,0,2,2,47.36000000000001,-2.09,CN(C)C(=O)Oc1cc(C)nn1c2ccccc2,Cc1cc(OC(=O)N(C)C)n(-c2ccccc2)n1,GEDIWDLJKRKBFT-UHFFFAOYSA-N,GEDIWDLJKRKBFT-UHFFFAOYSA-N,-2.09,,-0.0,0,1.0,-2.09,1.0 +280,o-Hydroxybenzamide,-1.942,1,137.13799999999998,2,1,1,63.32000000000001,-1.82,NC(=O)c1ccccc1O,NC(=O)c1ccccc1O,SKZKKFZAGNVIMN-UHFFFAOYSA-N,SKZKKFZAGNVIMN-UHFFFAOYSA-N,-1.82,,-0.0,0,1.0,-1.82,1.0 +281,o-Nitrotoluene,-2.589,1,137.138,0,1,1,43.14,-2.33,Cc1ccccc1N(=O)=O,Cc1ccccc1[N+](=O)[O-],PLAZTCDQAHEYBI-UHFFFAOYSA-N,PLAZTCDQAHEYBI-UHFFFAOYSA-N,-2.33,,-0.0,0,1.0,-2.33,1.0 +282,"5,5-Diisopropylbarbital",-1.942,1,212.249,2,1,2,75.27000000000001,-2.766,O=C1NC(=O)NC(=O)C1(C(C)C)C(C)C,CC(C)C1(C(C)C)C(=O)NC(=O)NC1=O,NSQYIEOSFPLTJA-UHFFFAOYSA-N,NSQYIEOSFPLTJA-UHFFFAOYSA-N,-2.766,,-0.0,0,1.0,-2.766,1.0 +283,2-Ethyltoluene,-3.2960000000000003,1,120.19499999999996,0,1,1,0.0,-3.21,CCc1ccccc1C,CCc1ccccc1C,HYFLWBNQFMXCPA-UHFFFAOYSA-N,HYFLWBNQFMXCPA-UHFFFAOYSA-N,-3.21,,-0.0,0,0.0,-3.21,0.0 +284,1-Chloroheptane,-3.003,1,134.65,0,0,5,0.0,-4.0,CCCCCCCCl,CCCCCCCCl,DZMDPHNGKBEVRE-UHFFFAOYSA-N,DZMDPHNGKBEVRE-UHFFFAOYSA-N,-4.0,,-0.0,0,0.0,-4.0,0.0 +285,Barbital,-1.265,1,184.195,2,1,2,75.27000000000001,-2.4,O=C1NC(=O)NC(=O)C1(CC)CC,CCC1(CC)C(=O)NC(=O)NC1=O,FTOAOBMCPZCFFF-UHFFFAOYSA-N,FTOAOBMCPZCFFF-UHFFFAOYSA-N,-2.4,,-0.0,0,1.0,-2.4,1.0 +286,Bibenzyl ,-4.301,2,182.266,0,2,3,0.0,-4.62,C(Cc1ccccc1)c2ccccc2,c1ccc(CCc2ccccc2)cc1,QWUWMCYKGHVNAV-UHFFFAOYSA-N,QWUWMCYKGHVNAV-UHFFFAOYSA-N,-4.62,,-0.0,0,0.0,-4.62,0.0 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diff --git a/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/aurka_chembl_base_smiles_union_short_with_rdkit_scaled_descriptors.csv b/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/aurka_chembl_base_smiles_union_short_with_rdkit_scaled_descriptors.csv new file mode 100644 index 00000000..9e8abf60 --- /dev/null +++ b/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/aurka_chembl_base_smiles_union_short_with_rdkit_scaled_descriptors.csv @@ -0,0 +1,201 @@ 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diff --git a/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/delaney-processed_curated_fit_short_with_rdkit_scaled_descriptors.csv b/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/delaney-processed_curated_fit_short_with_rdkit_scaled_descriptors.csv new file mode 100644 index 00000000..d056ae71 --- /dev/null +++ b/atomsci/ddm/test/integrative/import_transformer/data/scaled_descriptors/delaney-processed_curated_fit_short_with_rdkit_scaled_descriptors.csv @@ -0,0 +1,201 @@ +Compound 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+RTI 23,CCN2c1cc(OC)cc(C)c1NC(=O)c3cccnc23 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+2-Chlorotoluene,Cc1ccccc1Cl,0,0.9242505787037038,-0.33067129629629627,0.9242505787037038,0.44351851851851776,0.06268770825084556,15.823249999999994,14.941249999999998,15.752953488,42,0,0.06385044413527102,-0.08405299355229609,0.08405299355229609,0.06385044413527102,0.203125,0.296875,0.390625,3.6807357198826747,47.471916475878245,1.4955127018922192,1.3597455591261534,0.5792366773784602,0.8344283100678678,0.6798727795630767,0.2896183386892301,0.14862277956307668,0.1958683386892301,0.08021708467230752,0.11565125401692257,0.03715569489076918,0.04896708467230752,-0.061250000000000006,2.094639700632517,0.18896314229450825,0.27317010236609646,1.014610261986002,7.991800620243744,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.7160245016528,2.307220289581996,1.1418636227814984,0.6853792780851101,1.1994137366489426,1.4501174862790644,0.0,0.0,0.0,0.8564865146619599,0.0,34.7557596360915,0.0,0.0,0.0,0.0,0.0,11.600939890232516,0.0,0.0,6.851892117295679,1.8948451731110667,24.16967483065318,5.022633313741326,0.0,0.0,0.0,5.200956469173397,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.649531222928007,6.3305314429012345,0.698695023148148,0.0,-0.05543981481481472,-0.0665316358024691,0.0,-0.2814448302469135,0.0,-0.33067129629629627,0.0,0.14285714285714285,8,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,1,2.6484200000000007,4.523625,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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+"1,1,2-Trichloroethane",ClCC(Cl)Cl,0,1.3214506172839506,-0.4785493827160493,1.3214506172839506,2.2692901234567895,0.09605150969974732,26.681000000000004,26.0762,26.386006627199997,32,0,0.12082872501594434,-0.12377171147587997,0.12377171147587997,0.12082872501594434,0.4,0.44000000000000006,0.44000000000000006,4.020391543176799,13.501955000865388,1.4,1.0267786838055364,0.8803360514166089,0.65,0.46338934190276815,0.39016802570830456,0.07098038523709835,0.2986554414255808,0.014285714285714282,0.1285714285714286,0.0,0.0,0.174,1.253297578463043,0.449548501781652,0.6150677618069815,4.87,10.045780664674925,0.0,0.962464265890348,0.0,0.0,0.0,0.0,0.0,0.0,0.0,34.802819670697545,0.0,0.0,0.548303422468088,1.440573055908786,0.8224551337021321,6.960563934139509,0.0,0.0,0.0,0.962464265890348,5.832106723373709,0.0,0.0,0.0,0.0,0.0,0.0,34.802819670697545,2.1288856105650895,0.0,0.0,0.8224551337021321,0.0,0.0,0.0,0.0,0.0,2.1288856105650895,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.783019067841643,3.9470679012345684,2.952006172839506,-0.4538580246913579,0.0,0.0,-0.4785493827160493,0.0,0.0,0.0,0.0,1.0,5,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,2.0289,5.242799999999999,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +2-bromonaphthalene,c1c(Br)ccc2ccccc12 ,0,0.7120265151515152,-0.04148800934515218,0.7120265151515152,0.033449074074073826,0.05623226114894806,18.824545454545458,18.183090909090904,18.72482839309091,54,0,0.06411674422553322,-0.06162969636397052,0.06411674422553322,0.06162969636397052,0.09090909090909091,0.15702479338842976,0.23140495867768596,3.2161246449441507,51.56374976119334,1.2521366083542054,1.1252694975462934,0.6330873647385392,0.7532244381985159,0.5853620215004194,0.3392709550965423,0.17627111240951032,0.24836186418745143,0.11086282893202788,0.14690820482099845,0.06894257624447546,0.09597660816120338,-0.07454545454545455,2.350090679770045,0.2359677749953213,0.24233327983706834,1.2864543031768623,7.619582614368016,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.194730676024165,2.077935076750562,0.40661086507567396,0.8723008993810492,0.8723008993810492,2.427490211534449,0.0,0.0,0.0,0.0,0.0,46.76965046947548,0.0,0.0,0.0,0.0,0.0,15.929943897949348,0.0,0.0,0.0,0.8723008993810492,46.76965046947548,0.0,10.772448428929591,0.0,0.0,1.098621583211508,0.0,0.0,45.45726148307848,0.0,0.0,0.0,0.0,0.0,15.929943897949348,0.8723008993810492,4.901771144695251,0.27320076831237544,0.0,-0.010683922558922506,0.0,0.0,-0.21731829347900755,0.0,0.0,0.0,0.0,11,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,2,3.6023000000000014,4.695272727272729,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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,OC1CCCCC1,0,1.0794642857142858,-0.5138392857142858,1.0794642857142858,3.5708333333333337,0.06947498342054466,14.308714285714276,12.580714285714283,14.298402143428572,42,0,0.21044493502697398,-0.39313138246784024,0.39313138246784024,0.21044493502697398,0.22448979591836735,0.32653061224489793,0.40816326530612246,3.7616554503537105,50.811549542712804,2.243872397312364,2.2011783272091234,0.486892612923409,1.1515228816828316,1.0874817765279707,0.2434463064617045,0.13630344931884736,0.13630344931884736,0.06815172465942368,0.06815172465942368,0.03407586232971184,0.03407586232971184,-0.005714285714285714,2.244719052146579,0.09824875439680758,0.3305867456449512,1.4750627509406329,8.756954351231485,0.7298297415867446,0.0,0.0,0.2044570938903763,0.0,0.0,0.0,0.0,0.0,0.0,2.7312535291411626,1.8208356860941084,1.9582265088145998,1.0643966547314583,2.8838789012828046,0.0,1.4311996572326342,0.0,0.0,5.420663219085269,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.8028608393271193,0.0,0.0,6.706138374931336,0.0,0.0,0.0,0.0,2.89,5.420663219085269,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.1088081911072125,2.3585062535864365,12.687857142857144,0.0,0.0,0.5276785714285714,0.0,0.0,0.0,-3.072678571428572,0.0,0.0,1.0,7,1,1,1,0,1,0,0,0,1,1,1,1,1,0,1,1,1.3114,4.155971428571426,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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+Pyridazine,c1ccnnc1,0,1.164567901234568,-0.06003086419753086,1.164567901234568,0.3472222222222221,0.07542619051633219,13.34833333333333,12.676333333333332,13.339574687999999,30,0,0.08598140941410494,-0.15917583224964102,0.15917583224964102,0.08598140941410494,0.19444444444444445,0.2777777777777778,0.3611111111111111,3.3421538077705124,39.1132763960928,1.287269106521933,1.149071198499986,0.48240453183331927,0.7709829429477049,0.5662022659166596,0.2328689325833263,0.11226779962499649,0.11226779962499649,0.054050566479164903,0.054050566479164903,0.02598361657291579,0.02598361657291579,-0.15333333333333335,1.9321601728882982,0.17094684546566016,0.24996713993427988,0.5163568727137455,6.909839108410996,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10.197363616602075,0.0,0.0,2.0141395692210984,2.0576317369382537,0.9138390374468135,0.9138390374468135,0.0,0.0,1.6995606027670125,0.0,0.0,0.0,24.430627836956113,0.0,0.0,0.0,0.0,0.0,0.0,1.6995606027670125,0.0,0.0,0.9138390374468135,24.430627836956113,0.0,0.0,0.0,4.296666666666667,0.0,0.0,0.0,24.430627836956113,0.0,0.0,0.0,0.0,0.0,10.197363616602075,0.9138390374468135,4.626666666666666,0.0,0.0,1.0535802469135802,0.0,0.0,-0.1157407407407407,-0.12006172839506173,0.0,0.0,0.0,6,0,2,0,0,0,0,1,1,2,0,2,0,0,0,0,1,0.4765999999999999,3.6719999999999993,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +4-Chlorotoluene,Cc1ccc(Cl)cc1,0,0.9242505787037038,-0.3345293209876543,0.9242505787037038,0.3629629629629625,0.06268770825084556,15.823249999999994,14.941249999999998,15.752953488,42,0,0.06385012839856423,-0.08434413556465584,0.08434413556465584,0.06385012839856423,0.1875,0.265625,0.328125,3.6807357198826742,47.471916475878245,1.4955127018922194,1.3597455591261534,0.5792366773784602,0.834428310067868,0.6798727795630767,0.2896183386892301,0.14862277956307668,0.1958683386892301,0.07431138978153834,0.09793416934461505,0.03715569489076918,0.04896708467230752,-0.061250000000000006,2.094639700632517,0.18896314229450825,0.27317010236609646,1.2392064029119825,7.991800620243745,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.6561535996570127,2.3670911915777837,1.1418636227814984,0.6853792780851101,1.1994137366489426,1.4501174862790644,0.0,0.0,0.0,0.8564865146619599,0.0,34.7557596360915,0.0,0.0,0.0,0.0,0.0,11.600939890232516,0.0,0.0,6.851892117295679,1.8948451731110667,24.16967483065318,5.022633313741326,0.0,0.0,0.0,4.573127304955731,0.0,0.0,5.022633313741326,0.0,0.0,0.0,0.0,0.0,0.0,2.649531222928007,6.344134897014362,0.6889495937263793,0.0,-0.04537037037037031,-0.0744328703703703,0.0,-0.2836130401234567,0.0,-0.3345293209876543,0.0,0.14285714285714285,8,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,1,2.6484200000000007,4.523625000000001,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +2-Chlorobutane,CCC(C)Cl,0,1.4207947530864198,-0.6625385802469136,1.4207947530864198,3.1892361111111116,0.08687512049005953,18.513799999999993,16.6994,18.407855593599997,32,0,0.04686899348538178,-0.12332141991372317,0.12332141991372317,0.04686899348538178,0.52,0.6799999999999999,0.72,5.300082077607573,37.16645878435746,2.4,2.2755928946018456,0.6267786838055363,1.15,1.0877964473009227,0.26338934190276814,0.08779644730092273,0.16338934190276816,0.02194911182523068,0.04084733547569204,0.0,0.0,0.057999999999999996,1.8096798835528565,0.11024281397612219,0.5046200466200467,4.289999999999999,10.156415619882278,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.600939890232516,1.3703784234591359,2.644963403725012,3.5379933025878776,0.0,2.467365401106396,2.320187978046503,0.0,0.0,0.0,5.08596972866563,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.600939890232516,1.070627901481482,0.0,0.0,6.482707228290543,0.0,0.0,0.0,0.0,0.0,5.08596972866563,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.787553379152898,12.38601697530864,1.031560185185185,0.0,-0.6378472222222223,0.0,0.0,0.0,-0.6625385802469136,-1.2949691358024693,0.0,1.0,5,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,2.0237,5.121199999999997,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +Hexylbenzene ,CCCCCCc1ccccc1,0,0.6721279368228773,-0.34918179366654084,0.6721279368228773,0.9285309108530153,0.0481208675639106,13.523000000000012,12.010999999999997,13.511737548,66,0,0.06256033944053933,-0.06538238174359431,0.06538238174359431,0.06256033944053933,0.10416666666666667,0.17361111111111113,0.23611111111111113,3.8743316190381134,66.79534897253417,2.038675134594813,2.0,0.5,1.0771188733785786,1.0,0.25,0.13541666666666666,0.13541666666666666,0.07291666666666667,0.07291666666666667,0.0390625,0.0390625,-0.065,2.559621506674956,0.1143807603673143,0.48482954794042304,3.5646069197864145,8.409210400741431,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.681896696633358,1.5257751078629795,1.4849884358510714,0.5711493984042585,2.05613783425533,0.0,0.0,0.0,0.0,3.226376385328548,0.0,35.77554503001347,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5310770751107816,0.0,5.215058102114518,30.212093538316473,0.0,0.0,0.0,0.0,6.207671804496338,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.05613783425533,11.518692524796817,0.0,0.0,0.0,-0.1093657486142605,0.0,-0.4528196084273035,-1.6847954022143845,-0.3133784322075344,0.0,0.5,12,0,0,0,0,0,1,0,1,0,0,0,6,0,0,0,1,3.809400000000003,4.524000000000004,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +1-Chloroheptane,CCCCCCCCl,0,0.945534336419753,-0.511701388888889,0.945534336419753,3.47953691893424,0.05003906099322773,16.83125,14.941249999999998,16.76077852,50,0,0.04351941808357943,-0.12671696964453744,0.12671696964453744,0.04351941808357943,0.171875,0.265625,0.34375,6.370990656665334,49.96527438819279,2.4375,2.3597455591261536,0.5792366773784602,1.1875,1.1486227795630768,0.2583683386892301,0.08993638978153835,0.11355916934461505,0.03715569489076918,0.04896708467230752,0.014671597445384588,0.02057729233615376,0.03625,2.2407403893236806,0.11084075190789221,0.9112499999999994,6.289999999999997,9.788340930451005,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.600939890232516,4.04294896532665,0.7966156126661724,3.2991856332408758,0.0,2.570172292819162,1.4501174862790644,0.0,0.0,0.0,4.839564577992822,5.832106723373709,0.0,0.0,0.0,0.0,0.0,0.0,11.600939890232516,0.7290133404217136,0.0,0.0,7.40973687081199,0.0,0.0,0.0,0.0,0.0,5.568577918414535,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.020289779098227,13.72663364545278,0.6367509543362097,0.0,0.0,0.0,-0.43494211486678,0.0,-2.4848198144998745,-0.460983781533447,0.0,1.0,8,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,3.1956000000000016,4.934875000000001,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +3-Pentanoyloxymethylphenytoin,O=C1N(COC(=O)CCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3,0,0.5324647885567346,-0.16352154616844342,0.5324647885567346,0.6210349035734599,0.022340281334863675,13.570999999999989,12.74966666666667,13.561405821629652,140,0,0.32786640076668144,-0.4435958605585316,0.4435958605585316,0.32786640076668144,0.0438957475994513,0.06584362139917695,0.08641975308641976,2.4131793159605692,61.53449624791192,1.4267430132374146,1.2973118649205693,0.4824970501057544,0.8083983798992882,0.6605877152003522,0.25513535981146496,0.1704132291284157,0.1704132291284157,0.116575276301774,0.116575276301774,0.07695661518291537,0.07695661518291537,-0.11666666666666661,3.169611288447935,0.20143417018889948,0.297543411732642,3.556381478076956,6.986754082258555,0.3720991224847565,0.0,0.5049513835783112,0.0,5.907179729351506,12.000419800289922,0.35515090252383863,9.694446914922299,0.0,0.0,2.7277408924169637,0.648141773508273,0.6929537743281984,0.6092260249645421,1.8266017134784578,0.6632444270237565,0.0,0.37813789200514275,0.0,1.1670219286543395,6.682920025902854,71.55109006002695,0.0,0.0,0.1966597538254954,4.794537184071822,0.0,0.0,1.0922381217183244,0.7357356509010012,0.0,2.44012961052787,60.42418707663295,1.4118420783282006,0.0,0.0,2.804074074074074,5.105960176791751,0.5327263537857581,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.736862953800049,1.1184359910334376,6.641983425900621,0.0,1.4833982365177831,-0.02300129272494296,-0.24367107706203017,-0.18622675784117407,-0.5526567316497687,-0.4785971667569625,-0.3078953030501894,0.0,0.2857142857142857,27,1,6,0,1,1,2,0,2,4,1,6,8,0,1,1,3,3.172900000000002,3.6861370370370383,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +Methaqualone,Cc1ccccc1n3c(C)nc2ccccc2c3=O,0,0.7084547181445676,-0.17072496469347978,0.7084547181445676,0.17602088057445187,0.03500636164102657,13.173736842105267,12.430999999999997,13.163716477263158,94,0,0.2654677569072256,-0.2681674320933315,0.2681674320933315,0.2654677569072256,0.05817174515235458,0.09972299168975068,0.1440443213296399,2.7693762072875114,72.96827950909301,1.335124253633207,1.2264566042875673,0.48961449902440946,0.7854385582188061,0.6353767438937803,0.2669556912622014,0.18543087121886206,0.18543087121886206,0.12660428709715235,0.12660428709715235,0.08365540191769132,0.08365540191769132,-0.12736842105263155,2.6914058359136095,0.19955548621686212,0.2291608247823854,1.8340679152322232,6.862127507208993,0.0,0.30654760515789087,0.0,0.0,5.559266895052007,0.0,0.4927177279927988,4.9839785209472085,0.0,0.0,1.590110186227183,1.9681311499620127,0.4328711230011222,1.4503357713750724,1.0100326203359515,0.5738381543200556,0.0,0.5026883246704507,0.0,0.7212518018205978,0.0,70.08100973012712,0.0,5.687386274683562,0.2925929944764214,0.0,0.0,0.0,0.5026883246704507,0.0,13.703784234591359,1.6093934618989472,53.13388684537818,0.0,16.59031120676462,0.0,1.8363157894736843,5.030556209863751,0.2523440623195696,0.0,4.567099647791355,0.0,0.0,0.0,0.0,0.0,4.9839785209472085,1.0100326203359515,5.805396677989512,0.0,0.9055760432828099,-0.04030686836137962,-0.21708952993153077,-0.059183176194454366,-0.3787497690228712,0.0,-0.34020478127085807,0.0,0.125,19,0,3,0,0,0,2,1,3,3,0,3,3,0,0,0,3,3.0025400000000015,4.042842105263159,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +m-Xylene ,Cc1cccc(C)c1,0,0.9491030092592593,-0.3579282407407407,0.9491030092592593,0.7886574074074069,0.05946971066754013,13.270999999999992,12.010999999999997,13.25978129,42,0,0.06283107966352146,-0.06174087142677341,0.06283107966352146,0.06174087142677341,0.171875,0.25,0.3125,3.834543489724933,54.88960951404367,1.8080127018922192,1.75,0.5,0.9858439182435164,0.875,0.25,0.15625,0.15625,0.078125,0.078125,0.04296875,0.04296875,-0.0975,2.0941531485135068,0.11775020874897647,0.24921808903590698,1.0935372033822537,8.026374043746042,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.412072226755896,1.7129730293239198,1.0280689171276651,0.6853792780851101,1.713448195212775,0.0,0.0,0.0,0.0,1.7129730293239198,0.0,35.29657781404717,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.703784234591359,3.1043110681370236,24.16967483065318,0.0,0.0,0.0,0.0,6.125045256079816,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.713448195212775,9.133738425925925,0.0,0.0,0.0,-0.19916087962962953,0.0,-0.4062210648148147,0.0,-0.7158564814814814,0.0,0.25,8,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,1,2.30344,4.4895,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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diff --git a/atomsci/ddm/test/integrative/import_transformer/test_generate_transformers.py b/atomsci/ddm/test/integrative/import_transformer/test_generate_transformers.py new file mode 100644 index 00000000..887a61b5 --- /dev/null +++ b/atomsci/ddm/test/integrative/import_transformer/test_generate_transformers.py @@ -0,0 +1,199 @@ +import atomsci.ddm.utils.generate_transformers as gt +import atomsci.ddm.pipeline.parameter_parser as pp +import atomsci.ddm.pipeline.model_pipeline as mp +import atomsci.ddm.pipeline.transformations as trans +import atomsci.ddm.pipeline.compare_models as cm +import atomsci.ddm.utils.model_file_reader as mfr +from sklearn.preprocessing import PowerTransformer +from sklearn.pipeline import Pipeline + +import os +import shutil +import pandas as pd +import pickle as pkl +import tempfile +import pytest + +def rel_path(filename): + """ + Returns the absolute path to `filename` relative to the location of this file. + """ + return os.path.join(os.path.dirname(os.path.abspath(__file__)), filename) + +def clean(temp_dir, transformers_pkl_path): + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir) + + if os.path.exists(transformers_pkl_path): + os.remove(transformers_pkl_path) + +def test_transformer_generation(): + descriptor_type = 'rdkit_scaled' + temp_root = rel_path('temp') + transformers_pkl_path = rel_path('feature_transformers.pkl') + clean(temp_root, transformers_pkl_path) + assert not os.path.exists(temp_root) + os.makedirs(temp_root, exist_ok=True) + + prepared_H1 = gt.prepare_csv_and_descriptor_with_dummy_response( + rel_path('data/H1_std_short.csv'), + descriptor_type=descriptor_type, + temp_root=temp_root, + split_uuid='test-split' + ) + + assert os.path.exists(temp_root) + assert os.path.exists(os.path.join(temp_root, 'H1_std_short.csv')) + assert os.path.exists(os.path.join(temp_root, 'H1_std_short_train_valid_test_scaffold_test-split.csv')) + assert 'dummy_response' in pd.read_csv(os.path.join(temp_root, 'H1_std_short.csv')).columns + + H1_config = { + 'dataset_key':prepared_H1, + 'previously_split':'True', + 'id_col':'compound_id', + 'smiles_col':'base_rdkit_smiles', + 'response_cols':'dummy_response', + 'split_uuid':'test-split', + 'splitter':'scaffold', + } + + prepared_delaney = gt.prepare_csv_and_descriptor_with_dummy_response( + rel_path('data/delaney-processed_curated_fit_short.csv'), + descriptor_type=descriptor_type, + temp_root=temp_root + ) + + assert os.path.exists(os.path.join(temp_root, 'scaled_descriptors/delaney-processed_curated_fit_short_with_rdkit_scaled_descriptors.csv')) + + delaney_config = { + 'dataset_key': prepared_delaney, + 'id_col':'Compound ID', + 'smiles_col':'smiles', + 'response_cols':'dummy_response', + } + + aurk_config = { + 'dataset_key': rel_path('data/aurka_chembl_base_smiles_union_short.csv'), + 'id_col':'compound_id', + 'smiles_col':'base_rdkit_smiles', + 'response_cols':'pIC50', + } + + transformer_dataset_key_configs = [H1_config, delaney_config, aurk_config] + combined_dataset = gt.load_all_datasets(transformer_dataset_key_configs, + featurizer='computed_descriptors', + descriptor_type=descriptor_type) + + # aurka and delaney should have 200 each. H1 uses only 200 compounds from the training set + # some compounds fail to featurize. There should be less than 600 compouds total + assert len(combined_dataset) < 600 + + # fit transformers + gt.build_and_save_feature_transformers_from_csvs( + transformer_dataset_key_configs, + dest_pkl_path=transformers_pkl_path, + featurizer='computed_descriptors', + descriptor_type=descriptor_type, + feature_transform_type='PowerTransformer', + powertransformer_standardize='False', + ) + + with open(transformers_pkl_path, 'rb') as transformers_pkl_file: + saved_transformers = pkl.load(transformers_pkl_file) + + assert 'transformers_x' in saved_transformers + assert 'params' in saved_transformers + + assert transformer_dataset_key_configs == saved_transformers['params']['transformer_dataset_key_configs'] + assert saved_transformers['params']['featurizer'] == 'computed_descriptors' + assert saved_transformers['params']['descriptor_type'] == descriptor_type + assert saved_transformers['params']['feature_transform_type'] == 'PowerTransformer' + + # test load transformers + # first create a params json this uses a RobustScaler, we will check + # that the PowerTransformer is used instead + params_json = { + "dataset_key" : rel_path('data/H1_std_short.csv'), + "datastore" : "False", + "uncertainty": "False", + "splitter": "scaffold", + "previously_split": "True", + "split_uuid": "test-split", + "prediction_type": "regression", + "response_cols" : "pKi_mean", + "id_col": "compound_id", + "smiles_col" : "base_rdkit_smiles", + "result_dir": temp_root, + "model_type": "RF", + "featurizer": "ecfp", + "descriptor_type": 'rdkit_raw', + "feature_transform_type": "RobustScaler", + "feature_transform_path": transformers_pkl_path, + "save_results": "False", + "verbose": "False", + "seed":"0" + } + + # this one will fail because it has the wrong featurizer + with pytest.raises(ValueError) as excinfo: + bad_params1 = pp.wrapper(params_json) + model_pipeline = mp.ModelPipeline(bad_params1) + model_pipeline.train_model() + assert 'Loaded transformers do not match featurizer.' in str(excinfo.value) + + # This one will fail because it ahas the wrong descriptor type + params_json['featurizer'] = 'computed_descriptors' + with pytest.raises(ValueError) as excinfo: + bad_params2 = pp.wrapper(params_json) + model_pipeline = mp.ModelPipeline(bad_params2) + model_pipeline.train_model() + assert 'Loaded transformers do not match descriptor_type.' in str(excinfo.value) + + # This one will succeed. + params_json['descriptor_type'] = descriptor_type + test_params = pp.wrapper(params_json) + model_pipeline = mp.ModelPipeline(test_params) + model_pipeline.train_model() + + # check that the PowerTransformer was used instead of RobustScaler + assert model_pipeline.params.feature_transform_type == 'PowerTransformer' + transformers_x = model_pipeline.model_wrapper.transformers_x + assert len(transformers_x[0])==1 + assert isinstance(transformers_x[0][0], trans.SklearnPipelineWrapper) + assert isinstance(transformers_x[0][0].sklearn_pipeline, Pipeline) + scaler = transformers_x[0][0].sklearn_pipeline.named_steps['PowerTransformer'] + assert isinstance(scaler, PowerTransformer) + + # check that the saved transformer pkl is a PowerTransformer + # reload the pipeline and see that it was loaded correctly works + results = cm.get_filesystem_perf_results(temp_root, pred_type='regression') + model_path = results['model_path'].values[0] + pred_params = { + 'featurizer': 'computed_descriptors', + 'result_dir': tempfile.mkdtemp(), + 'id_col': 'compound_id', + 'smiles_col':'base_rdkit_smiles' + } + pred_params = pp.wrapper(pred_params) + reloaded_pipeline = mp.create_prediction_pipeline_from_file(pred_params, reload_dir=None, model_path=model_path) + + assert reloaded_pipeline.params.feature_transform_type == 'PowerTransformer' + rl_formers_x = reloaded_pipeline.model_wrapper.transformers_x + scaler = rl_formers_x[0][0].sklearn_pipeline.named_steps['PowerTransformer'] + assert isinstance(scaler, PowerTransformer) + + # check that model metadata shows that this was a PowerTransformer + # check that standardize is set to false + reader = mfr.ModelFileReader(model_path) + assert not reader.get_powertransformer_standardize() + + # check that the dataset_key configs were also saved in the parameters + loaded_trans_dskey_configs = reader.get_transformer_dataset_key_configs() + assert loaded_trans_dskey_configs == transformer_dataset_key_configs + + + # cleanup + clean(temp_root, transformers_pkl_path) + +if __name__ == '__main__': + test_transformer_generation() \ No newline at end of file diff --git a/atomsci/ddm/test/test_datasets/Molport_test.csv b/atomsci/ddm/test/test_datasets/Molport_test.csv new file mode 100644 index 00000000..d91d501c --- /dev/null +++ b/atomsci/ddm/test/test_datasets/Molport_test.csv @@ -0,0 +1,101 @@ +compound_id,SMILES,base_rdkit_smiles,dummy_response +MolPort-008-351-270,O=CN1CCN(C(=O)Cn2ccc3ccc(Br)cc32)CC1,O=CN1CCN(C(=O)Cn2ccc3ccc(Br)cc32)CC1,0 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a/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_random_e28e63fb-4d30-4cef-b303-7f0622f64688.csv b/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_random_e28e63fb-4d30-4cef-b303-7f0622f64688.csv new file mode 100644 index 00000000..e1a1a456 --- /dev/null +++ b/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_random_e28e63fb-4d30-4cef-b303-7f0622f64688.csv @@ -0,0 +1,101 @@ +cmpd_id,subset,fold +MolPort-008-351-270,train,0 +MolPort-008-351-272,train,0 +MolPort-008-351-275,train,0 +MolPort-008-351-276,train,0 +MolPort-008-351-280,train,0 +MolPort-008-351-283,train,0 +MolPort-008-351-285,train,0 +MolPort-008-351-286,train,0 +MolPort-008-351-288,train,0 +MolPort-008-351-291,train,0 +MolPort-008-351-292,train,0 +MolPort-008-351-293,train,0 +MolPort-008-351-294,train,0 +MolPort-008-351-295,train,0 +MolPort-008-351-296,train,0 +MolPort-008-351-297,train,0 +MolPort-008-351-298,train,0 +MolPort-008-351-299,train,0 +MolPort-008-351-301,train,0 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+MolPort-008-351-290,test,0 +MolPort-008-351-302,test,0 +MolPort-008-351-331,test,0 +MolPort-008-351-338,test,0 +MolPort-008-351-355,test,0 +MolPort-008-351-360,test,0 +MolPort-008-351-368,test,0 +MolPort-008-351-369,test,0 +MolPort-008-351-371,test,0 +MolPort-008-351-378,test,0 +MolPort-008-351-379,test,0 +MolPort-008-351-380,test,0 +MolPort-009-019-503,test,0 \ No newline at end of file diff --git a/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_scaffold_e28e63fb-4d30-4cef-b303-7f0622f64688.csv b/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_scaffold_e28e63fb-4d30-4cef-b303-7f0622f64688.csv new file mode 100644 index 00000000..ca3b982d --- /dev/null +++ b/atomsci/ddm/test/test_datasets/Molport_test_train_valid_test_scaffold_e28e63fb-4d30-4cef-b303-7f0622f64688.csv @@ -0,0 +1,101 @@ +cmpd_id,subset,fold +MolPort-008-351-282,train,0 +MolPort-008-351-288,train,0 +MolPort-008-351-290,train,0 +MolPort-008-351-292,train,0 +MolPort-008-351-297,train,0 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new file mode 100644 index 00000000..901a99f3 --- /dev/null +++ b/atomsci/ddm/test/test_datasets/scaled_descriptors/Molport_test_with_rdkit_raw_descriptors.csv @@ -0,0 +1,101 @@ 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diff --git a/atomsci/ddm/test/unit/test_featurization.py b/atomsci/ddm/test/unit/test_featurization.py index df1f6a88..3ac601c7 100644 --- a/atomsci/ddm/test/unit/test_featurization.py +++ b/atomsci/ddm/test/unit/test_featurization.py @@ -1,10 +1,12 @@ from argparse import Namespace import pytest +import numpy as np import atomsci.ddm.pipeline.featurization as feat import deepchem as dc from atomsci.ddm.pipeline import model_datasets as md import pandas as pd - +import unittest.mock as mock +import atomsci.ddm.pipeline.parameter_parser as param_parser try: from mol_vae_features import MoleculeVAEFeaturizer mol_vae_supported = True @@ -229,5 +231,128 @@ def test_compute_moe(): assert moe_df is not None +def test_compute_all_mordred_descrs_returns_none_on_valueerror(caplog, monkeypatch): + # Fake "res_df" returned by calc.pandas(...) + fake_df = mock.MagicMock() + fake_df.fill_missing.side_effect = ValueError("boom") + + # Fake calculator + fake_calc = mock.MagicMock() + fake_calc.pandas.return_value = fake_df + + # Patch the calculator factory used inside the function + monkeypatch.setattr(feat, "get_mordred_calculator", lambda ignore_3D: fake_calc) + + with caplog.at_level("ERROR"): + out = feat.compute_all_mordred_descrs(mols=[object()]) + + assert out is None + assert "Mordred descriptor calculation failed in MordredDataFrame.fill_missing" in caplog.text + +def test_not_implemented(): + params = param_parser.wrapper({"dataset_key": "fake.csv"}) + f = feat.Featurization(params) + with pytest.raises(NotImplementedError): + f.get_feature_count() + + fp = feat.PersistentFeaturization(params) + with pytest.raises(NotImplementedError): + fp.featurize_data( + dset_df=None, + params=None, + contains_responses=False + ) + + +def test_scale_by_heavyatomcount_uses_specified_heavy_atom_col(monkeypatch): + params = param_parser.wrapper({ + "dataset_key": "fake.csv", + "featurizer": "compouted_descriptors", + "descriptor_type": "rdkit_raw",}) + obj = feat.ComputedDescriptorFeaturization(params) + + # Force a small, known descriptor set for the test + monkeypatch.setattr( + obj.__class__, + "desc_type_cols", + {"demo": ["MolWt_per_heavyatom", "B_log_scaled", "C"]}, + raising=False, + ) + + df = pd.DataFrame( + { + "id": [1, 2], + "HA": [10, 5], # specified heavy atom column + "MolWt": [100.0, 50.0], # unscaled input for _per_heavyatom + "B": [1.0, np.e], # unscaled input for _log_scaled + "C": [7.0, 9.0], # pass-through + } + ) + + out = obj.scale_by_heavyatomcount_and_log_scale(df, descr_type="demo", heavy_atom_col="HA") + + # Columns exist (do not assert full column order, nondesc_cols order is set-derived) + assert {"id", "HA", "MolWt_per_heavyatom", "B_log_scaled", "C"}.issubset(out.columns) + + # Values are correct + assert np.allclose(out["MolWt_per_heavyatom"].to_numpy(), np.array([10.0, 10.0])) + assert np.allclose(out["B_log_scaled"].to_numpy(), np.array([0.0, 1.0])) + assert np.allclose(out["C"].to_numpy(), df["C"].to_numpy()) + assert np.allclose(out["HA"].to_numpy(), df["HA"].to_numpy()) + assert np.allclose(out["id"].to_numpy(), df["id"].to_numpy()) + + +def test_scale_by_heavyatomcount_raises_runtimeerror_when_cannot_infer_heavy_atom_col(monkeypatch): + params = param_parser.wrapper({ + "dataset_key": "fake.csv", + "featurizer": "compouted_descriptors", + "descriptor_type": "rdkit_raw",}) + obj = feat.ComputedDescriptorFeaturization(params) + + # No "HeavyAtomCount" or "nHeavyAtom" in descr_cols, and we do not pass heavy_atom_col + monkeypatch.setattr( + obj.__class__, + "desc_type_cols", + {"demo": ["MolWt_per_heavyatom"]}, + raising=False, + ) + + df = pd.DataFrame({"MolWt": [100.0]}) + + with pytest.raises(RuntimeError, match=r"Could not infer heavy atom column"): + obj.scale_by_heavyatomcount_and_log_scale(df, descr_type="demo") + +def test_scale_by_heavyatomcount_and_log_scale_raises_on_negative_log_feature(monkeypatch): + params = param_parser.wrapper({ + "dataset_key": "fake.csv", + "featurizer": "compouted_descriptors", + "descriptor_type": "rdkit_raw",}) + obj = feat.ComputedDescriptorFeaturization(params) + + # No "HeavyAtomCount" or "nHeavyAtom" in descr_cols, and we do not pass heavy_atom_col + monkeypatch.setattr( + obj.__class__, + "desc_type_cols", + {"test_type": ["SomeFeature_log_scaled", "HeavyAtomCount"]}, + raising=False, + ) + + # Build a DataFrame where: + # - HeavyAtomCount is present so the function can infer heavy atom counts + # - SomeFeature has a negative value, and desc_type_cols includes SomeFeature_log_scaled + df = pd.DataFrame({ + "HeavyAtomCount": [10, 12], + "SomeFeature": [-1.0, 2.0] # negative value triggers ValueError in log scaling + }) + + with pytest.raises(ValueError) as excinfo: + obj.scale_by_heavyatomcount_and_log_scale( + desc_df=df, + descr_type="test_type" # uses desc_type_cols defined above + ) + + # Optionally check the error message + assert "Cannot log scale SomeFeature" in str(excinfo.value) + if __name__ == '__main__': test_compute_moe() \ No newline at end of file diff --git a/atomsci/ddm/test/unit/test_generate_transformers.py b/atomsci/ddm/test/unit/test_generate_transformers.py new file mode 100644 index 00000000..de3c4873 --- /dev/null +++ b/atomsci/ddm/test/unit/test_generate_transformers.py @@ -0,0 +1,71 @@ +import atomsci.ddm.utils.generate_transformers as gt +import tempfile +import pytest + +def test_filter_outlier_features(): + dataset_key = '../test_datasets/Molport_test.csv' + id_col = 'compound_id' + smiles_col = 'base_rdkit_smiles' + response_cols = 'dummy_response' + + a, b, c = gt.filter_outlier_features( + dataset_key, id_col, smiles_col, response_cols, + featurizer='computed_descriptors', descriptor_type='rdkit_raw', + threshold=1e10) + + expected_a = set([ + 'MolPort-008-351-295', 'MolPort-008-351-371', 'MolPort-008-351-357', + 'MolPort-008-351-359', 'MolPort-008-351-292', 'MolPort-008-351-372', + 'MolPort-008-351-322', 'MolPort-008-351-275', 'MolPort-008-351-338', + 'MolPort-008-351-358', 'MolPort-008-351-382', 'MolPort-008-351-297', + 'MolPort-008-351-375', 'MolPort-008-351-354', 'MolPort-008-351-277', + 'MolPort-008-351-353', 'MolPort-008-351-318', 'MolPort-008-351-280', + 'MolPort-008-351-339', 'MolPort-008-351-343', 'MolPort-008-351-313', + 'MolPort-008-351-296', 'MolPort-008-351-336', 'MolPort-008-351-317', + 'MolPort-008-351-283', 'MolPort-008-351-373', 'MolPort-008-351-363', + 'MolPort-008-351-294', 'MolPort-008-351-290', 'MolPort-008-351-321', + 'MolPort-008-351-344', 'MolPort-008-351-305', 'MolPort-008-351-328', + 'MolPort-008-351-379', 'MolPort-008-351-352', 'MolPort-008-351-319']) + expected_b = ['Ipc']*len(expected_a) + + assert set(a) == expected_a + assert list(b) == expected_b + assert all([val > 1e10 for val in c]) + +def test_filter_outlier_MW(): + dataset_key = '../test_datasets/Molport_test.csv' + smiles_col = 'base_rdkit_smiles' + + outliers = gt.filter_outlier_MW(dataset_key, smiles_col, threshold=450, workers=1) + + expected_outliers = ['O=C(CC[C@@H]1NC(=O)N(Cc2ccc3c(c2)OCO3)C1=O)NCCC(c1ccccc1)c1ccccc1', + 'Cc1[nH]c(=O)[nH]c(=O)c1S(=O)(=O)N1CCCC(C(=O)Nc2ccc(C(F)(F)F)cc2)C1', + 'O=C1CC2(C(=O)Nc3ccc(Br)cc32)c2sc(C(=O)O)c(-c3ccccc3)c2N1', + 'O=C(CC[C@@H]1NC(=O)N(Cc2ccc3c(c2)OCO3)C1=O)NC1CCN(Cc2ccccc2)CC1', + 'COC(=O)C[C@H](NC(=O)N1CCc2nc[nH]c2C1c1cccc(Cl)c1Cl)C(=O)OC', + 'Cc1[nH]c(=O)[nH]c(=O)c1S(=O)(=O)N1CCCC(C(=O)NCCc2ccc(Cl)cc2Cl)C1', + 'CN1CCN(C2(CNC(=O)C[C@@H]3NC(=O)N(Cc4ccc5c(c4)OCO5)C3=O)CCCCC2)CC1', + 'O=C(C[C@@H]1NC(=O)N(Cc2ccc3c(c2)OCO3)C1=O)NCc1ccc(Cl)cc1Cl'] + + assert outliers == expected_outliers + +def test_prepare_csv_and_descriptor_with_dummy_response(): + dataset_key = '../test_datasets/Molport_test.csv' + temp_root = tempfile.mkdtemp() + + # this should raise a RuntimeError + # because the split_uuid is used for two split files + # this should almost never happen in real life, + # but we want to make sure the error is raised if it does + with pytest.raises(RuntimeError): + gt.prepare_csv_and_descriptor_with_dummy_response( + csv_path=dataset_key, + descriptor_type='rdkit_raw', + temp_root = temp_root, + split_uuid='e28e63fb-4d30-4cef-b303-7f0622f64688' + ) + +if __name__ == "__main__": + test_filter_outlier_features() + test_filter_outlier_MW() + test_prepare_csv_and_descriptor_with_dummy_response() \ No newline at end of file diff --git a/atomsci/ddm/test/unit/test_perf_plots.py b/atomsci/ddm/test/unit/test_perf_plots.py index 2ccc6ac4..14ab1b87 100644 --- a/atomsci/ddm/test/unit/test_perf_plots.py +++ b/atomsci/ddm/test/unit/test_perf_plots.py @@ -2,12 +2,16 @@ from contextlib import contextmanager import matplotlib.pyplot as plt from unittest.mock import MagicMock +import tarfile +import json import pytest import pandas as pd from matplotcheck.base import PlotTester -from atomsci.ddm.pipeline.perf_plots import plot_pred_vs_actual_from_df +import atomsci.ddm.pipeline.perf_plots as perf_plots +import io +import numpy as np # --- Fixtures --- @pytest.fixture @@ -92,7 +96,7 @@ def test_plot_pred_vs_actual_from_df_basic(mock_data, mock_plot): df = mock_data with mock_plot() as mock_scatter: - ax = plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred') + ax = perf_plots.plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred') # Verify the plot was created assert ax is not None @@ -124,7 +128,7 @@ def test_plot_pred_vs_actual_from_df_with_std(mock_data, mock_plot): """ df = mock_data with mock_plot() as mock_scatter: - ax = plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', std_col='avg_pIC50_std') + ax = perf_plots.plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', std_col='avg_pIC50_std') pt = PlotTester(ax) assert ax is not None @@ -149,7 +153,7 @@ def test_plot_pred_vs_actual_from_df_with_label(mock_data, mock_plot): """ df = mock_data with mock_plot() as mock_scatter: - ax = plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', label='Test Label') + ax = perf_plots.plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', label='Test Label') pt = PlotTester(ax) assert ax is not None @@ -176,7 +180,7 @@ def test_plot_pred_vs_actual_from_df_with_threshold(mock_data, mock_plot): df = mock_data with mock_plot() as mock_scatter: - ax = plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', threshold=7.0) + ax = perf_plots.plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', threshold=7.0) pt = PlotTester(ax) assert ax is not None @@ -203,7 +207,7 @@ def test_plot_pred_vs_actual_from_df_with_all_options(mock_data, mock_plot): """ df = mock_data with mock_plot() as mock_scatter: - ax = plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', std_col='avg_pIC50_std', label='Test Label', threshold=7.0) + ax = perf_plots.plot_pred_vs_actual_from_df(df, actual_col='avg_pIC50_actual', pred_col='avg_pIC50_pred', std_col='avg_pIC50_std', label='Test Label', threshold=7.0) pt = PlotTester(ax) assert ax is not None @@ -211,4 +215,345 @@ def test_plot_pred_vs_actual_from_df_with_all_options(mock_data, mock_plot): pt.assert_axis_label_contains("y", "avg_pIC50_pred") pt.assert_plot_type("scatter") assert ax.get_title() == 'Test Label' - assert any(line.get_linestyle() == '--' for line in ax.get_lines()) \ No newline at end of file + assert any(line.get_linestyle() == '--' for line in ax.get_lines()) + + +def _make_model_tarball(tmp_path, metadata: dict, name="model.tar.gz"): + """Create a minimal tar.gz containing model_metadata.json.""" + model_path = tmp_path / name + meta_bytes = json.dumps(metadata).encode("utf-8") + + with tarfile.open(model_path, mode="w:gz") as tar: + ti = tarfile.TarInfo(name="model_metadata.json") + ti.size = len(meta_bytes) + tar.addfile(ti, io.BytesIO(meta_bytes)) + return str(model_path) + + +def _write_csv(path, df: pd.DataFrame): + df.to_csv(path, index=False) + return str(path) + + +# ------------------------------- +# Tests: merge_response_cols_from_original +# ------------------------------- + +def test_merge_response_cols_happy_path_adds_missing_response_cols(): + feat_df = pd.DataFrame( + { + "cmpd_id": ["1", "2", "3"], + "feat1": [0.1, 0.2, 0.3], + } + ) + orig_df = pd.DataFrame( + { + "cmpd_id": ["1", "2", "3"], + "y": [10.0, 20.0, 30.0], + } + ) + + out = perf_plots.merge_response_cols_from_original( + feat_df, orig_df, id_col="cmpd_id", response_cols=["y"] + ) + + assert "y" in out.columns + assert out.loc[out.cmpd_id == "2", "y"].iloc[0] == 20.0 + + +def test_merge_response_cols_does_not_overwrite_existing_columns(): + feat_df = pd.DataFrame( + { + "cmpd_id": ["1", "2"], + "y": [999.0, 999.0], # pre-existing, should not be overwritten + } + ) + orig_df = pd.DataFrame({"cmpd_id": ["1", "2"], "y": [1.0, 2.0]}) + + out = perf_plots.merge_response_cols_from_original( + feat_df, orig_df, id_col="cmpd_id", response_cols=["y"] + ) + + assert out["y"].tolist() == [999.0, 999.0] + + +def test_merge_response_cols_raises_when_id_col_missing_in_feat(): + feat_df = pd.DataFrame({"not_id": [1, 2], "feat": [0.1, 0.2]}) + orig_df = pd.DataFrame({"cmpd_id": [1, 2], "y": [1.0, 2.0]}) + + with pytest.raises(KeyError): + perf_plots.merge_response_cols_from_original( + feat_df, orig_df, id_col="cmpd_id", response_cols=["y"] + ) + + +def test_merge_response_cols_missing_frac_exceeds_threshold_raises(): + # orig has 4 unique IDs, feat only contains 1 => 75% missing + feat_df = pd.DataFrame({"cmpd_id": ["1"], "feat": [0.1]}) + orig_df = pd.DataFrame({"cmpd_id": ["1", "2", "3", "4"], "y": [1, 2, 3, 4]}) + + with pytest.raises(ValueError, match="exceeds allowed"): + perf_plots.merge_response_cols_from_original( + feat_df, + orig_df, + id_col="cmpd_id", + response_cols=["y"], + max_missing_frac=0.10, + sample_n=2, + ) + + +def test_merge_response_cols_extra_ids_warns_by_default(): + feat_df = pd.DataFrame({"cmpd_id": ["1", "X"], "feat": [0.1, 0.9]}) + orig_df = pd.DataFrame({"cmpd_id": ["1"], "y": [1.0]}) + + # Keep missing_frac low: orig_set - feat_set = {} so missing_frac=0 + with pytest.warns(UserWarning, match="contains 1 unique compound IDs not found"): + perf_plots.merge_response_cols_from_original( + feat_df, + orig_df, + id_col="cmpd_id", + response_cols=["y"], + max_missing_frac=0.50, + error_on_extra_feat_ids=False, + ) + + +def test_merge_response_cols_extra_ids_raises_if_configured(): + feat_df = pd.DataFrame({"cmpd_id": ["1", "X"], "feat": [0.1, 0.9]}) + orig_df = pd.DataFrame({"cmpd_id": ["1"], "y": [1.0]}) + + with pytest.raises(ValueError, match="not found in original"): + perf_plots.merge_response_cols_from_original( + feat_df, + orig_df, + id_col="cmpd_id", + response_cols=["y"], + max_missing_frac=0.50, + error_on_extra_feat_ids=True, + ) + + +def test_merge_response_cols_coerces_id_to_str_by_default(): + feat_df = pd.DataFrame({"cmpd_id": [1, 2], "feat": [0.1, 0.2]}) + orig_df = pd.DataFrame({"cmpd_id": ["1", "2"], "y": [10.0, 20.0]}) + + out = perf_plots.merge_response_cols_from_original( + feat_df, orig_df, id_col="cmpd_id", response_cols=["y"], coerce_id_to_str=True + ) + assert out["y"].tolist() == [10.0, 20.0] + + +def test_merge_response_cols_returns_feat_unchanged_if_no_responses_available(): + feat_df = pd.DataFrame({"cmpd_id": ["1"], "feat": [0.1]}) + orig_df = pd.DataFrame({"cmpd_id": ["1"], "other": [9]}) + + out = perf_plots.merge_response_cols_from_original( + feat_df, orig_df, id_col="cmpd_id", response_cols=["y"] + ) + assert list(out.columns) == ["cmpd_id", "feat"] + + +# ------------------------------- +# Tests: plot_pred_vs_actual_from_file +# ------------------------------- + +def test_plot_pred_vs_actual_from_file_raises_for_classification(tmp_path): + # Minimal config for classification should raise early + dataset_csv = tmp_path / "data.csv" + _write_csv(dataset_csv, pd.DataFrame({"cmpd_id": ["1"], "smiles": ["C"], "y": [1]})) + + cfg = { + "model_parameters": {"prediction_type": "classification", "model_type": "rf", "featurizer": "ecfp"}, + "training_dataset": {"dataset_key": str(dataset_csv), "response_cols": ["y"], "id_col": "cmpd_id", "smiles_col": "smiles"}, + "splitting_parameters": {"split_uuid": "uuid", "splitter": "random", "split_strategy": "train_valid_test"}, + "training_metrics": [], + } + model_path = _make_model_tarball(tmp_path, cfg) + + with pytest.raises(ValueError, match="only be called for regression models"): + perf_plots.plot_pred_vs_actual_from_file(model_path) + + +def test_plot_pred_vs_actual_from_file_builds_figure_and_r2_titles(tmp_path, monkeypatch): + # Prepare dataset and split file + dataset_csv = tmp_path / "data.csv" + df = pd.DataFrame( + { + "cmpd_id": ["1", "2", "3", "4", "5", "6"], + "smiles": ["C", "CC", "CCC", "CCCC", "CCO", "CO"], + "y": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0], + } + ) + _write_csv(dataset_csv, df) + + split_uuid = "split-uuid-123" + split_csv = tmp_path / f"anything_{split_uuid}_split.csv" + split_df = pd.DataFrame( + { + "cmpd_id": ["1", "2", "3", "4", "5", "6"], + "subset": ["train", "train", "valid", "valid", "test", "test"], + } + ) + _write_csv(split_csv, split_df) + + cfg = { + "model_parameters": { + "prediction_type": "regression", + "model_type": "rf", + "featurizer": "ecfp", + # keep uncertainty absent so error_bars becomes False even if requested + }, + "training_dataset": { + "dataset_key": str(dataset_csv), + "response_cols": ["y"], + "id_col": "cmpd_id", + "smiles_col": "smiles", + }, + "splitting_parameters": { + "split_uuid": split_uuid, + "splitter": "random", + "split_strategy": "train_valid_test", + }, + "training_metrics": [], + } + model_path = _make_model_tarball(tmp_path, cfg) + + # Patch safe_extract to allow tar extraction without depending on atomsci futils implementation + def _safe_extract_passthrough(tar, path): + tar.extractall(path=path) + + monkeypatch.setattr(perf_plots.futils, "safe_extract", _safe_extract_passthrough) + + # Patch prediction to return a dataframe with required columns: y_actual, y_pred and subset + def _fake_predict_from_model_file(model_path_in, input_df, **kwargs): + out = input_df.copy() + out["y_actual"] = out["y"].astype(float) + out["y_pred"] = out["y_actual"] # perfect predictions => r2 = 1.0 on each subset + return out + + monkeypatch.setattr(perf_plots.pfm, "predict_from_model_file", _fake_predict_from_model_file) + + fig = perf_plots.plot_pred_vs_actual_from_file(model_path, plot_size=3, error_bars=True) + assert fig is not None + + # Expect 1 response * 3 subsets = 3 axes + assert len(fig.axes) == 3 + + titles = [ax.get_title() for ax in fig.axes] + # First plot has "y train, R^2 = 1.000" style title, others "valid, R^2 = 1.000", "test, ..." + assert any("R^2" in t for t in titles) + assert any("1.000" in t for t in titles) + + plt.close(fig) + + +def test_plot_pred_vs_actual_from_file_uses_external_training_data(tmp_path, monkeypatch): + # Original dataset_key in metadata points to one CSV, but we override via external_training_data. + dataset_csv_original = tmp_path / "data_original.csv" + dataset_csv_external = tmp_path / "data_external.csv" + + df_orig = pd.DataFrame({"cmpd_id": ["1"], "smiles": ["C"], "y": [1.0]}) + _write_csv(dataset_csv_original, df_orig) + + df_ext = pd.DataFrame({ + "cmpd_id": ["1", "2", "3"], + "smiles": ["C", "CC", "CCC"], + "y": [2.0, 3.0, 4.0], + }) + _write_csv(dataset_csv_external, df_ext) + + split_uuid = "uuid2" + split_csv = tmp_path / f"split_{split_uuid}.csv" + _write_csv(split_csv, pd.DataFrame({ + "cmpd_id": ["1", "2", "3"], + "subset": ["train", "valid", "test"], + })) + + cfg = { + "model_parameters": {"prediction_type": "regression", "model_type": "rf", "featurizer": "ecfp"}, + "training_dataset": {"dataset_key": str(dataset_csv_original), "response_cols": ["y"], "id_col": "cmpd_id", "smiles_col": "smiles"}, + "splitting_parameters": {"split_uuid": split_uuid, "splitter": "random", "split_strategy": "train_valid_test"}, + "training_metrics": [], + } + model_path = _make_model_tarball(tmp_path, cfg) + + def _safe_extract_passthrough(tar, path): + tar.extractall(path=path) + + monkeypatch.setattr(perf_plots.futils, "safe_extract", _safe_extract_passthrough) + + seen = {"used_y": None} + + def _fake_predict_from_model_file(model_path_in, input_df, **kwargs): + # Record which dataset was used by checking y + seen["used_y"] = float(input_df["y"].iloc[0]) + out = input_df.copy() + out["y_actual"] = out["y"].astype(float) + out["y_pred"] = out["y_actual"] + return out + + monkeypatch.setattr(perf_plots.pfm, "predict_from_model_file", _fake_predict_from_model_file) + + fig = perf_plots.plot_pred_vs_actual_from_file( + model_path, + external_training_data=str(dataset_csv_external), + plot_size=3, + ) + + assert seen["used_y"] == 2.0 # proves override worked + plt.close(fig) + + +def test_plot_pred_vs_actual_from_file_descriptor_path_rewrite(tmp_path, monkeypatch): + # If featurizer is descriptors/computed_descriptors, function rewrites dataset path to scaled_descriptors/..._with__descriptors.csv + data_dir = tmp_path + orig = data_dir / "data.csv" + _write_csv(orig, pd.DataFrame({"cmpd_id": ["1"], "smiles": ["C"], "y": [1.0]})) + + # Create the expected rewritten file + scaled_dir = data_dir / "scaled_descriptors" + scaled_dir.mkdir() + rewritten = scaled_dir / "data_with_mordred_descriptors.csv" + + df_rewritten = pd.DataFrame({ + "cmpd_id": ["1", "2", "3"], + "smiles": ["C", "CC", "CCC"], + "y": [2.0, 3.0, 4.0], + }) + _write_csv(rewritten, df_rewritten) + + split_uuid = "uuid3" + split_csv = data_dir / f"split_{split_uuid}.csv" + _write_csv(split_csv, pd.DataFrame({ + "cmpd_id": ["1", "2", "3"], + "subset": ["train", "valid", "test"], + })) + + cfg = { + "model_parameters": {"prediction_type": "regression", "model_type": "rf", "featurizer": "descriptors"}, + "descriptor_specific": {"descriptor_type": "mordred"}, + "training_dataset": {"dataset_key": str(orig), "response_cols": ["y"], "id_col": "cmpd_id", "smiles_col": "smiles"}, + "splitting_parameters": {"split_uuid": split_uuid, "splitter": "random", "split_strategy": "train_valid_test"}, + "training_metrics": [], + } + model_path = _make_model_tarball(tmp_path, cfg) + + def _safe_extract_passthrough(tar, path): + tar.extractall(path=path) + + monkeypatch.setattr(perf_plots.futils, "safe_extract", _safe_extract_passthrough) + + # We must ensure merge_response_cols_from_original is exercised: rewritten df lacks y, orig has y. + def _fake_predict_from_model_file(model_path_in, input_df, **kwargs): + assert "y" in input_df.columns # confirms merge happened + out = input_df.copy() + out["y_actual"] = out["y"].astype(float) + out["y_pred"] = out["y_actual"] + return out + + monkeypatch.setattr(perf_plots.pfm, "predict_from_model_file", _fake_predict_from_model_file) + + fig = perf_plots.plot_pred_vs_actual_from_file(model_path, plot_size=3) + assert fig is not None + plt.close(fig) \ No newline at end of file diff --git a/atomsci/ddm/test/unit/test_transformers.py b/atomsci/ddm/test/unit/test_transformers.py index 69cfebd6..d3058c42 100644 --- a/atomsci/ddm/test/unit/test_transformers.py +++ b/atomsci/ddm/test/unit/test_transformers.py @@ -1,7 +1,61 @@ import atomsci.ddm.pipeline.transformations as trans +import atomsci.ddm.pipeline.parameter_parser as pp import numpy as np +import pandas as pd from deepchem.data import NumpyDataset +import pytest +from sklearn.preprocessing import RobustScaler, PowerTransformer + +def test_sklearn_pipeline_wrapper(): + """ + Creates a mock dataset. + Tests the SklearnTransformerWrapper with RobustScaler on X. + Tests the SklearnTransformerWrapper with PowerTransformer on y. + Tests the SklearnTransformerWrapper with RobustScaler on w. + Asserts that the transformed values match the expected values. + """ + # Create a mock dataset + X = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) + y = np.array([[1.0], [3.0], [5.0]]) + w = np.array([[1.0], [1.0], [1.0]]) + ids = np.array(range(len(y))) + dataset = NumpyDataset(X=X, y=y, w=w, ids=ids) + + # Test with RobustScaler on X + scaler = RobustScaler() + transformer = trans.SklearnPipelineWrapper(dataset, scaler, transform_X=True) + transformed_dataset = transformer.transform(dataset) + expected_transformed_X = scaler.fit_transform(X) + np.testing.assert_array_almost_equal(transformed_dataset.X, expected_transformed_X) + + # Test untransform on X + with pytest.raises(NotImplementedError) as exception_info: + untransformed_X = transformer.untransform(transformed_dataset.X) + assert str(exception_info.value) == 'SklearnPipelineWrapper does not support inverse transforms' + + # Test with PowerTransformer on y + power_transformer = PowerTransformer() + transformer = trans.SklearnPipelineWrapper(dataset, power_transformer, transform_y=True) + transformed_dataset = transformer.transform(dataset) + expected_transformed_y = power_transformer.fit_transform(y) + np.testing.assert_array_almost_equal(transformed_dataset.y, expected_transformed_y) + + # Test untransform on y + with pytest.raises(NotImplementedError) as exception_info: + untransformed_y = transformer.untransform(transformed_dataset.y) + assert str(exception_info.value) == 'SklearnPipelineWrapper does not support inverse transforms' + + # Test with RobustScaler on w + transformer = trans.SklearnPipelineWrapper(dataset, scaler, transform_w=True) + transformed_dataset = transformer.transform(dataset) + expected_transformed_w = scaler.fit_transform(w) + np.testing.assert_array_almost_equal(transformed_dataset.w, expected_transformed_w) + + # Test untransform on w + with pytest.raises(NotImplementedError) as exception_info: + untransformed_w = transformer.untransform(transformed_dataset.w) + assert str(exception_info.value) == 'SklearnPipelineWrapper does not support inverse transforms' def test_no_missing_values(): """ @@ -162,3 +216,87 @@ def test_normalization_transformer_missing_data_transform_X(): expected_transformed_X = (X - expected_X_means) / expected_X_stds np.testing.assert_array_almost_equal(transformed_dataset.X, expected_transformed_X, decimal=6) +def test_create_feature_transformers(): + """Test the `create_feature_transformers` when params.transformers is None.""" + + params = pp.wrapper({}) + params.transformers = None + transformers_x = trans.create_feature_transformers( + params, + featurization = None, + train_dset = None + ) + + assert transformers_x == [] + +def test_zero_out_inf_nan_numpy_with_nonfinite_replaces_and_copies(): + x = np.array([1.0, np.nan, np.inf, -np.inf, 2.5], dtype=float) + y = trans.zero_out_inf_nan(x) + + assert isinstance(y, np.ndarray) + np.testing.assert_array_equal(y, np.array([1.0, 0.0, 0.0, 0.0, 2.5], dtype=float)) + + # Ensure it is a copy and original not mutated + assert y is not x + np.testing.assert_array_equal(x, np.array([1.0, np.nan, np.inf, -np.inf, 2.5], dtype=float)) + + +def test_zero_out_inf_nan_numpy_without_nonfinite_no_change_but_copy(): + x = np.array([1.0, 2.0, 3.0], dtype=float) + y = trans.zero_out_inf_nan(x) + + assert isinstance(y, np.ndarray) + np.testing.assert_array_equal(y, x) + assert y is not x + + +def test_zero_out_inf_nan_series_with_nonfinite_replaces_preserves_index_and_name(): + s = pd.Series([1.0, np.nan, np.inf, -np.inf], index=["a", "b", "c", "d"], name="vals") + out = trans.zero_out_inf_nan(s) + + assert isinstance(out, pd.Series) + assert out.name == "vals" + assert list(out.index) == ["a", "b", "c", "d"] + np.testing.assert_array_equal(out.values, np.array([1.0, 0.0, 0.0, 0.0], dtype=float)) + + # Original not mutated + assert np.isnan(s.loc["b"]) + assert np.isposinf(s.loc["c"]) + assert np.isneginf(s.loc["d"]) + + +def test_zero_out_inf_nan_series_without_nonfinite_no_change_values(): + s = pd.Series([1.0, 2.0], index=[10, 20], name="ok") + out = trans.zero_out_inf_nan(s) + + assert isinstance(out, pd.Series) + assert out.name == "ok" + assert list(out.index) == [10, 20] + np.testing.assert_array_equal(out.values, s.values) + + +def test_zero_out_inf_nan_dataframe_with_nonfinite_replaces_preserves_index_and_columns(): + df = pd.DataFrame( + {"c1": [1.0, np.nan], "c2": [np.inf, 4.0]}, + index=["r1", "r2"], + ) + out = trans.zero_out_inf_nan(df) + + assert isinstance(out, pd.DataFrame) + assert list(out.index) == ["r1", "r2"] + assert list(out.columns) == ["c1", "c2"] + np.testing.assert_array_equal(out.values, np.array([[1.0, 0.0], [0.0, 4.0]], dtype=float)) + + # Original not mutated + assert np.isnan(df.loc["r2", "c1"]) + assert np.isposinf(df.loc["r1", "c2"]) + + +def test_zero_out_inf_nan_dataframe_without_nonfinite_no_change_values(): + df = pd.DataFrame({"a": [1.0, 2.0], "b": [3.0, 4.0]}, index=[0, 1]) + out = trans.zero_out_inf_nan(df) + + assert isinstance(out, pd.DataFrame) + assert list(out.index) == [0, 1] + assert list(out.columns) == ["a", "b"] + np.testing.assert_array_equal(out.values, df.values) \ No newline at end of file diff --git a/atomsci/ddm/utils/generate_transformers.py b/atomsci/ddm/utils/generate_transformers.py new file mode 100644 index 00000000..7992c66e --- /dev/null +++ b/atomsci/ddm/utils/generate_transformers.py @@ -0,0 +1,235 @@ +import pickle +import glob +import os +import pandas as pd +import atomsci.ddm.pipeline.featurization as feat +import atomsci.ddm.pipeline.parameter_parser as pp +import atomsci.ddm.pipeline.model_datasets as model_datasets +import atomsci.ddm.pipeline.transformations as trans +import atomsci.ddm.utils.struct_utils as struct_utils +from deepchem.data import NumpyDataset +import numpy as np +import shutil +import sklearn.utils as sku +import logging +logging.basicConfig(format='%(asctime)-15s %(message)s') +log = logging.getLogger('ATOM') + + +def prepare_csv_and_descriptor_with_dummy_response(csv_path, descriptor_type, temp_root, split_uuid='split_uuid'): + """ + Copies the csv file and its descriptor file to a temp directory, preserving structure, + and adds a 'dummy_response' column of zeros to both. + + Args: + csv_path (str): Path to the original CSV file. + descriptor_type (str): Descriptor type to look for in the descriptor file name. + temp_root (str): Root of the temporary directory to copy files into. + split_uuid (str): Unique identifier for the split file to look for and copy if it exists. + + Returns: + (str, str): Paths to the new CSV and descriptor files in the temp directory. + """ + # Find descriptor file + csv_dir = os.path.dirname(csv_path) + csv_base = os.path.splitext(os.path.basename(csv_path))[0] + descriptor_dir = os.path.join(csv_dir, 'scaled_descriptors') + descriptor_pattern = f"{csv_base}_with_{descriptor_type}_descriptors.csv" + descriptor_path = os.path.join(descriptor_dir, descriptor_pattern) + copy_descriptor_csv = os.path.exists(descriptor_path) + + # Find split file if it exists + split_pattern = os.path.join(csv_dir, f'{csv_base}_*_{split_uuid}.csv') + split_files = glob.glob(split_pattern) + if len(split_files)>1: + raise RuntimeError(f'Multiple splits found {split_files}') + if len(split_files)>0 and len(split_uuid)>0: + split_csv = split_files[0] + split_base = os.path.basename(split_csv) + temp_split_csv = os.path.join(temp_root, split_base) + shutil.copy(split_csv, temp_split_csv) + + # Prepare destination paths + temp_csv_path = os.path.join(temp_root, os.path.basename(csv_path)) + temp_descriptor_dir = os.path.join(temp_root, 'scaled_descriptors') + os.makedirs(temp_descriptor_dir, exist_ok=True) + temp_descriptor_path = os.path.join(temp_descriptor_dir, os.path.basename(descriptor_path)) + + # Copy and add dummy_response to CSV + df_csv = pd.read_csv(csv_path) + df_csv['dummy_response'] = 0 + df_csv.to_csv(temp_csv_path, index=False) + + # Copy and add dummy_response to descriptor file + if copy_descriptor_csv: + df_desc = pd.read_csv(descriptor_path) + df_desc['dummy_response'] = 0 + df_desc.to_csv(temp_descriptor_path, index=False) + + return temp_csv_path + +def load_all_datasets( + transformer_dataset_key_configs, + featurizer, + descriptor_type +): + """Loads datasets from configs and builds NumpyDataset + + Args: + csvs_or_tuples (list): List of csv file paths or (csv_file, split_uuid) tuples. + featurizer (str): The featurizer type (e.g., 'ecfp', 'graphconv', 'computed_descriptors', etc.). + descriptor_type (str): Descriptor type (e.g., 'moe', 'rdkit_raw', etc.). + + Returns: + NumpyDataset + """ + featurized_datasets = [] + for ds_config in transformer_dataset_key_configs: + # Prepare params for this dataset + params_dict = dict() + params_dict.update(ds_config) + params_dict['featurizer'] = featurizer + params_dict['descriptor_type'] = descriptor_type + params_dict['feature_transform_type'] = 'Identity' + + # check if there is a split_uuid in the config + split_uuid = params_dict.get('split_uuid', None) + + params = pp.wrapper(params_dict) + dataset = model_datasets.create_and_load_model_dataset(params, ds_client=None) + + # If split_uuid is provided, use only the training subset + if split_uuid: + dataset.split_dataset() + train_dset = dataset.train_valid_dsets[0][0] + featurized_datasets.append(train_dset) + else: + # this is a NumpyDataset with all data + featurized_datasets.append(dataset.dataset) + + # Combine all dataframes for fitting transformers + combined_dataset = NumpyDataset( + X=np.vstack([d.X for d in featurized_datasets]), + y=np.vstack([d.y for d in featurized_datasets]), + ids=np.concatenate([d.ids for d in featurized_datasets]), + w=np.concatenate([d.w for d in featurized_datasets]) + ) + + return combined_dataset + +def filter_outlier_features(dataset_key, id_col, smiles_col, response_cols, featurizer, descriptor_type, threshold=1e10): + """Looks for compounds with very large descriptor values. + + Args: + dataset_key_configs (list): List of dataset key configuration dictionaries. + featurizer (str): The featurizer type (e.g., 'ecfp', 'graphconv', 'computed_descriptors', etc.). + descriptor_type (str): Descriptor type (e.g., 'moe', 'rdkit_raw', etc.). + threshold (float): Threshold for filtering large descriptor values. + + Returns: + DataFrame with outlier compound_ids, descriptor column names, and descriptor values that exceed the threshold. + """ + dataset_key_config = { + 'dataset_key': dataset_key, + 'id_col': id_col, + 'smiles_col': smiles_col, + 'response_cols': response_cols + } + params_dict = dict() + params_dict.update(dataset_key_config) + params_dict['featurizer'] = featurizer + params_dict['descriptor_type'] = descriptor_type + params_dict['feature_transform_type'] = 'Identity' + params_dict['verbose'] = True + + params = pp.wrapper(params_dict) + dataset = model_datasets.create_and_load_model_dataset(params, ds_client=None) + + abs_X = np.abs(dataset.dataset.X) + try: + sku.assert_all_finite(abs_X) + except ValueError: + # data contains inf or nan values + log.warning("SklearnPipelineWrapper: data contains NaN or Inf; replacing with zeros") + abs_X = trans.zero_out_inf_nan(abs_X) + + + large_values = np.argwhere(abs_X > threshold) + feature_cols = np.array(dataset.featurization.get_feature_columns()) + + return dataset.dataset.ids[large_values[:,0]], [feature_cols[i] for i in large_values[:,1]], [abs_X[i,j] for i,j in large_values] + +def filter_outlier_MW(dataset_key, smiles_col, threshold=1000, workers=8): + """Filters datasets and looks for compounds with very large molecular weights. + + Args: + dataset_key (str): Path to the dataset CSV file. + smiles_col (str): Name of the column containing SMILES strings. + threshold (float): Threshold for filtering large molecular weights. + workers (int): Number of workers to use for parallel processing in calculating molecular weights. + workers (int): Number of workers to use for parallel processing in calculating molecular weights. + + Returns: + List of SMILES with molecular weights that exceed the threshold. + + """ + # molecular weight > 1000 + # calculate molecular weight here. + dataset_df = pd.read_csv(dataset_key) + mol_weights = struct_utils.mol_wt_from_smiles(dataset_df[smiles_col].to_list(), workers=workers) + mw_large = np.argwhere(np.array(mol_weights) > threshold) + + return dataset_df[smiles_col].iloc[mw_large.flatten()].tolist() + +def build_and_save_feature_transformers_from_csvs( + transformer_dataset_key_configs, + dest_pkl_path, + featurizer, + descriptor_type, + feature_transform_type, + **kwargs +): + """ + Build feature transformers_x from a list of csv files (or csv+split_uuid tuples) and save them as a pickle file, + including the params object used to create them. This function saves feature transformers suitable for use + for one fold. + + Args: + transformer_dataset_key_configs (list): A list of dictionaries that contain information about each dataset_key such + as id_col, smiles_col, response_cols, split_uuids, etc. + dest_pkl_path (str): Path to save the pickle file. + featurizer (str): The featurizer type (e.g., 'ecfp', 'graphconv', 'computed_descriptors', etc.). + descriptor_type (str): Descriptor type (e.g., 'moe', 'rdkit_raw', etc.). + feature_transform_type (str): The type of transformer to use (e.g., 'RobustScaler', 'PowerTransformer', etc.). + **kwargs: Additional keyword arguments for transformer params. + + Returns: + None + """ + combined_dataset = load_all_datasets(transformer_dataset_key_configs=transformer_dataset_key_configs, featurizer=featurizer, descriptor_type=descriptor_type) + + # Build a single params object for transformer fitting + params_dict = dict( + featurizer=featurizer, + feature_transform_type=feature_transform_type, + descriptor_type=descriptor_type, + **kwargs + ) + params = pp.wrapper(params_dict) + featurization = feat.create_featurization(params) + + # Build the feature transformers (transformers_x) + transformers_x = featurization.create_feature_transformer(combined_dataset, params) + + # Save both transformers_x and params to a pickle file + # copy processed params back into params_dict in case wrapper + # updates them. + for k in params_dict.keys(): + if k in params.__dict__: + params_dict[k] = params.__dict__[k] + + params_dict['transformer_dataset_key_configs'] = transformer_dataset_key_configs + with open(dest_pkl_path, 'wb') as f: + pickle.dump({'transformers_x': transformers_x, 'params': params_dict}, f) + + print(f"Feature transformers_x and params saved to {dest_pkl_path}") \ No newline at end of file diff --git a/atomsci/ddm/utils/hyperparam_search_wrapper.py b/atomsci/ddm/utils/hyperparam_search_wrapper.py index 5e1d999e..23c8e979 100755 --- a/atomsci/ddm/utils/hyperparam_search_wrapper.py +++ b/atomsci/ddm/utils/hyperparam_search_wrapper.py @@ -135,8 +135,6 @@ def reformat_filter_dict(filter_dict): 'training_dataset': {'bucket', 'dataset_key', 'dataset_oid', 'num_classes','feature_transform_type', 'response_transform_type', 'id_col', 'smiles_col', 'response_cols'}, - 'umap_specific': - {'umap_dim', 'umap_metric', 'umap_min_dist', 'umap_neighbors','umap_targ_wt'} } if filter_dict['model_type'] == 'NN': rename_dict['nn_specific'] = {'baseline_epoch', 'batch_size', 'best_epoch', 'bias_init_consts','dropouts', @@ -270,7 +268,6 @@ def __init__(self, params): self.hyperparam_layers = {'layer_sizes', 'dropouts', 'weight_init_stddevs', 'bias_init_consts'} self.hyperparam_keys = {'model_type', 'featurizer', 'splitter', 'learning_rate', 'weight_decay_penalty', 'rf_estimators', 'rf_max_features', 'rf_max_depth', - 'umap_dim', 'umap_targ_wt', 'umap_metric', 'umap_neighbors', 'umap_min_dist', 'xgb_learning_rate', 'xgb_gamma'} self.nn_specific_keys = {'learning_rate', 'layers','weight_decay_penalty'} diff --git a/atomsci/ddm/utils/model_file_reader.py b/atomsci/ddm/utils/model_file_reader.py index 87bfee9b..fb073869 100644 --- a/atomsci/ddm/utils/model_file_reader.py +++ b/atomsci/ddm/utils/model_file_reader.py @@ -91,6 +91,61 @@ def get_model_parameters(self): """ return self.metadata_dict.get("model_parameters") + def get_transformer_specific_parameters(self): + """Returns: + (str): embedding specific parameters + + """ + return self.metadata_dict.get("transformer_specific") + + def get_robustscaler_with_centering(self): + """Returns: + (bool): robustscaler_with_centering + """ + return self.get_transformer_specific_parameters().get("robustscaler_with_centering") + + def get_robustscaler_with_scaling(self): + """Returns: + (bool): robustscaler_with_scaling + """ + return self.get_transformer_specific_parameters().get("robustscaler_with_scaling") + + def get_robustscaler_quartile_range(self): + """Returns: + (list[float]): robustscaler_quartile_range + """ + return self.get_transformer_specific_parameters().get("robustscaler_quartile_range") + + def get_robustscaler_unit_variance(self): + """Returns: + (bool): robustscaler_unit_variance + """ + return self.get_transformer_specific_parameters().get("robustscaler_unit_variance") + + def get_powertransformer_method(self): + """Returns: + (str): powertransformer_method + """ + return self.get_transformer_specific_parameters().get("powertransformer_method") + + def get_powertransformer_standardize(self): + """Returns: + (bool): powertransformer_standardize + """ + return self.get_transformer_specific_parameters().get("powertransformer_standardize") + + def get_imputer_strategy(self): + """Returns: + (str): imputer_strategy + """ + return self.get_transformer_specific_parameters().get("imputer_strategy") + + def get_transformer_dataset_key_configs(self): + """Returns: + (str): transformer_dataset_key_configs + """ + return self.get_transformer_specific_parameters().get("transformer_dataset_key_configs") + def get_embedding_specific_parameters(self): """Returns: (str): embedding specific parameters @@ -104,7 +159,6 @@ def get_embedding_model_uuid(self): """ return self.get_embedding_specific_parameters().get("embedding_model_uuid") - def get_embedding_model_path(self): """Returns: (str): embedding_model_path