From 907c7f97d70de35c6639ad4f27de5ca4dc4225c1 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Wed, 14 Aug 2019 18:31:57 -0400 Subject: [PATCH 01/40] Added YSI 2.1 database (new mols, QSPR from PaDEL) --- databases/ysi_database_v2.1.csv | 568 ++++++++++++++++++++++++++++++++ 1 file changed, 568 insertions(+) create mode 100644 databases/ysi_database_v2.1.csv diff --git a/databases/ysi_database_v2.1.csv b/databases/ysi_database_v2.1.csv new file mode 100644 index 0000000..d911f9f --- /dev/null +++ b/databases/ysi_database_v2.1.csv @@ -0,0 +1,568 @@ 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By,Notes,TARGET,nAcid,ALogP,ALogp2,AMR,apol,naAromAtom,nAromBond,nAtom,nHeavyAtom,nH,nB,nC,nN,nO,nS,nP,nF,nCl,nBr,nI,nX,ATS0m,ATS1m,ATS2m,ATS3m,ATS4m,ATS5m,ATS6m,ATS7m,ATS8m,ATS0v,ATS1v,ATS2v,ATS3v,ATS4v,ATS5v,ATS6v,ATS7v,ATS8v,ATS0e,ATS1e,ATS2e,ATS3e,ATS4e,ATS5e,ATS6e,ATS7e,ATS8e,ATS0p,ATS1p,ATS2p,ATS3p,ATS4p,ATS5p,ATS6p,ATS7p,ATS8p,ATS0i,ATS1i,ATS2i,ATS3i,ATS4i,ATS5i,ATS6i,ATS7i,ATS8i,ATS0s,ATS1s,ATS2s,ATS3s,ATS4s,ATS5s,ATS6s,ATS7s,ATS8s,AATS0m,AATS1m,AATS2m,AATS3m,AATS4m,AATS5m,AATS6m,AATS7m,AATS8m,AATS0v,AATS1v,AATS2v,AATS3v,AATS4v,AATS5v,AATS6v,AATS7v,AATS8v,AATS0e,AATS1e,AATS2e,AATS3e,AATS4e,AATS5e,AATS6e,AATS7e,AATS8e,AATS0p,AATS1p,AATS2p,AATS3p,AATS4p,AATS5p,AATS6p,AATS7p,AATS8p,AATS0i,AATS1i,AATS2i,AATS3i,AATS4i,AATS5i,AATS6i,AATS7i,AATS8i,AATS0s,AATS1s,AATS2s,AATS3s,AATS4s,AATS5s,AATS6s,AATS7s,AATS8s,ATSC0c,ATSC1c,ATSC2c,ATSC3c,ATSC4c,ATSC5c,ATSC6c,ATSC7c,ATSC8c,ATSC0m,ATSC1m,ATSC2m,ATSC3m,ATSC4m,ATSC5m,ATSC6m,ATSC7m,ATSC8m,ATSC0v,ATSC1v,ATSC2v,ATSC3v,ATSC4v,ATSC5v,ATSC6v,ATSC7v,ATSC8v,ATSC0e,ATSC1e,ATSC2e,ATSC3e,ATSC4e,ATSC5e,ATSC6e,ATSC7e,ATSC8e,ATSC0p,ATSC1p,ATSC2p,ATSC3p,ATSC4p,ATSC5p,ATSC6p,ATSC7p,ATSC8p,ATSC0i,ATSC1i,ATSC2i,ATSC3i,ATSC4i,ATSC5i,ATSC6i,ATSC7i,ATSC8i,ATSC0s,ATSC1s,ATSC2s,ATSC3s,ATSC4s,ATSC5s,ATSC6s,ATSC7s,ATSC8s,AATSC0c,AATSC1c,AATSC2c,AATSC3c,AATSC4c,AATSC5c,AATSC6c,AATSC7c,AATSC8c,AATSC0m,AATSC1m,AATSC2m,AATSC3m,AATSC4m,AATSC5m,AATSC6m,AATSC7m,AATSC8m,AATSC0v,AATSC1v,AATSC2v,AATSC3v,AATSC4v,AATSC5v,AATSC6v,AATSC7v,AATSC8v,AATSC0e,AATSC1e,AATSC2e,AATSC3e,AATSC4e,AATSC5e,AATSC6e,AATSC7e,AATSC8e,AATSC0p,AATSC1p,AATSC2p,AATSC3p,AATSC4p,AATSC5p,AATSC6p,AATSC7p,AATSC8p,AATSC0i,AATSC1i,AATSC2i,AATSC3i,AATSC4i,AATSC5i,AATSC6i,AATSC7i,AATSC8i,AATSC0s,AATSC1s,AATSC2s,AATSC3s,AATSC4s,AATSC5s,AATSC6s,AATSC7s,AATSC8s,MATS1c,MATS2c,MATS3c,MATS4c,MATS5c,MATS6c,MATS7c,MATS8c,MATS1m,MATS2m,MATS3m,MATS4m,MATS5m,MATS6m,MATS7m,MATS8m,MATS1v,MATS2v,MATS3v,MATS4v,MATS5v,MATS6v,MATS7v,MATS8v,MATS1e,MATS2e,MATS3e,MATS4e,MATS5e,MATS6e,MATS7e,MATS8e,MATS1p,MATS2p,MATS3p,MATS4p,MATS5p,MATS6p,MATS7p,MATS8p,MATS1i,MATS2i,MATS3i,MATS4i,MATS5i,MATS6i,MATS7i,MATS8i,MATS1s,MATS2s,MATS3s,MATS4s,MATS5s,MATS6s,MATS7s,MATS8s,GATS1c,GATS2c,GATS3c,GATS4c,GATS5c,GATS6c,GATS7c,GATS8c,GATS1m,GATS2m,GATS3m,GATS4m,GATS5m,GATS6m,GATS7m,GATS8m,GATS1v,GATS2v,GATS3v,GATS4v,GATS5v,GATS6v,GATS7v,GATS8v,GATS1e,GATS2e,GATS3e,GATS4e,GATS5e,GATS6e,GATS7e,GATS8e,GATS1p,GATS2p,GATS3p,GATS4p,GATS5p,GATS6p,GATS7p,GATS8p,GATS1i,GATS2i,GATS3i,GATS4i,GATS5i,GATS6i,GATS7i,GATS8i,GATS1s,GATS2s,GATS3s,GATS4s,GATS5s,GATS6s,GATS7s,GATS8s,SpAbs_DzZ,SpMax_DzZ,SpDiam_DzZ,SpAD_DzZ,SpMAD_DzZ,EE_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,EE_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,EE_Dzv,SM1_Dzv,VE1_Dzv,VE2_Dzv,VE3_Dzv,VR1_Dzv,VR2_Dzv,VR3_Dzv,SpAbs_Dze,SpMax_Dze,SpDiam_Dze,SpAD_Dze,SpMAD_Dze,EE_Dze,SM1_Dze,VE1_Dze,VE2_Dze,VE3_Dze,VR1_Dze,VR2_Dze,VR3_Dze,SpAbs_Dzp,SpMax_Dzp,SpDiam_Dzp,SpAD_Dzp,SpMAD_Dzp,EE_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,EE_Dzi,SM1_Dzi,VE1_Dzi,VE2_Dzi,VE3_Dzi,VR1_Dzi,VR2_Dzi,VR3_Dzi,SpAbs_Dzs,SpMax_Dzs,SpDiam_Dzs,SpAD_Dzs,SpMAD_Dzs,EE_Dzs,SM1_Dzs,VE1_Dzs,VE2_Dzs,VE3_Dzs,VR1_Dzs,VR2_Dzs,VR3_Dzs,nBase,BCUTw-1l,BCUTw-1h,BCUTc-1l,BCUTc-1h,BCUTp-1l,BCUTp-1h,nBonds,nBonds2,nBondsS,nBondsS2,nBondsS3,nBondsD,nBondsD2,nBondsT,nBondsQ,nBondsM,bpol,SpMax1_Bhm,SpMax2_Bhm,SpMax3_Bhm,SpMax4_Bhm,SpMax5_Bhm,SpMax6_Bhm,SpMax7_Bhm,SpMax8_Bhm,SpMin1_Bhm,SpMin2_Bhm,SpMin3_Bhm,SpMin4_Bhm,SpMin5_Bhm,SpMin6_Bhm,SpMin7_Bhm,SpMin8_Bhm,SpMax1_Bhv,SpMax2_Bhv,SpMax3_Bhv,SpMax4_Bhv,SpMax5_Bhv,SpMax6_Bhv,SpMax7_Bhv,SpMax8_Bhv,SpMin1_Bhv,SpMin2_Bhv,SpMin3_Bhv,SpMin4_Bhv,SpMin5_Bhv,SpMin6_Bhv,SpMin7_Bhv,SpMin8_Bhv,SpMax1_Bhe,SpMax2_Bhe,SpMax3_Bhe,SpMax4_Bhe,SpMax5_Bhe,SpMax6_Bhe,SpMax7_Bhe,SpMax8_Bhe,SpMin1_Bhe,SpMin2_Bhe,SpMin3_Bhe,SpMin4_Bhe,SpMin5_Bhe,SpMin6_Bhe,SpMin7_Bhe,SpMin8_Bhe,SpMax1_Bhp,SpMax2_Bhp,SpMax3_Bhp,SpMax4_Bhp,SpMax5_Bhp,SpMax6_Bhp,SpMax7_Bhp,SpMax8_Bhp,SpMin1_Bhp,SpMin2_Bhp,SpMin3_Bhp,SpMin4_Bhp,SpMin5_Bhp,SpMin6_Bhp,SpMin7_Bhp,SpMin8_Bhp,SpMax1_Bhi,SpMax2_Bhi,SpMax3_Bhi,SpMax4_Bhi,SpMax5_Bhi,SpMax6_Bhi,SpMax7_Bhi,SpMax8_Bhi,SpMin1_Bhi,SpMin2_Bhi,SpMin3_Bhi,SpMin4_Bhi,SpMin5_Bhi,SpMin6_Bhi,SpMin7_Bhi,SpMin8_Bhi,SpMax1_Bhs,SpMax2_Bhs,SpMax3_Bhs,SpMax4_Bhs,SpMax5_Bhs,SpMax6_Bhs,SpMax7_Bhs,SpMax8_Bhs,SpMin1_Bhs,SpMin2_Bhs,SpMin3_Bhs,SpMin4_Bhs,SpMin5_Bhs,SpMin6_Bhs,SpMin7_Bhs,SpMin8_Bhs,C1SP1,C2SP1,C1SP2,C2SP2,C3SP2,C1SP3,C2SP3,C3SP3,C4SP3,SCH-3,SCH-4,SCH-5,SCH-6,SCH-7,VCH-3,VCH-4,VCH-5,VCH-6,VCH-7,SC-3,SC-4,SC-5,SC-6,VC-3,VC-4,VC-5,VC-6,SPC-4,SPC-5,SPC-6,VPC-4,VPC-5,VPC-6,SP-0,SP-1,SP-2,SP-3,SP-4,SP-5,SP-6,SP-7,ASP-0,ASP-1,ASP-2,ASP-3,ASP-4,ASP-5,ASP-6,ASP-7,VP-0,VP-1,VP-2,VP-3,VP-4,VP-5,VP-6,VP-7,AVP-0,AVP-1,AVP-2,AVP-3,AVP-4,AVP-5,AVP-6,AVP-7,Sv,Sse,Spe,Sare,Sp,Si,Mv,Mse,Mpe,Mare,Mp,Mi,CrippenLogP,CrippenMR,SpMax_Dt,SpDiam_Dt,SpAD_Dt,SpMAD_Dt,EE_Dt,VE1_Dt,VE2_Dt,VE3_Dt,VR1_Dt,VR2_Dt,VR3_Dt,ECCEN,nHBd,nwHBd,nHBa,nwHBa,nHBint2,nHBint3,nHBint4,nHBint5,nHBint6,nHBint7,nHBint8,nHBint9,nHBint10,nHsOH,nHdNH,nHsSH,nHsNH2,nHssNH,nHaaNH,nHsNH3p,nHssNH2p,nHsssNHp,nHtCH,nHdCH2,nHdsCH,nHaaCH,nHCHnX,nHCsats,nHCsatu,nHAvin,nHother,nHmisc,nsLi,nssBe,nssssBem,nsBH2,nssBH,nsssB,nssssBm,nsCH3,ndCH2,nssCH2,ntCH,ndsCH,naaCH,nsssCH,nddC,ntsC,ndssC,naasC,naaaC,nssssC,nsNH3p,nsNH2,nssNH2p,ndNH,nssNH,naaNH,ntN,nsssNHp,ndsN,naaN,nsssN,nddsN,naasN,nssssNp,nsOH,ndO,nssO,naaO,naOm,nsOm,nsF,nsSiH3,nssSiH2,nsssSiH,nssssSi,nsPH2,nssPH,nsssP,ndsssP,nddsP,nsssssP,nsSH,ndS,nssS,naaS,ndssS,nddssS,nssssssS,nSm,nsCl,nsGeH3,nssGeH2,nsssGeH,nssssGe,nsAsH2,nssAsH,nsssAs,ndsssAs,nddsAs,nsssssAs,nsSeH,ndSe,nssSe,naaSe,ndssSe,nssssssSe,nddssSe,nsBr,nsSnH3,nssSnH2,nsssSnH,nssssSn,nsI,nsPbH3,nssPbH2,nsssPbH,nssssPb,SHBd,SwHBd,SHBa,SwHBa,SHBint2,SHBint3,SHBint4,SHBint5,SHBint6,SHBint7,SHBint8,SHBint9,SHBint10,SHsOH,SHdNH,SHsSH,SHsNH2,SHssNH,SHaaNH,SHsNH3p,SHssNH2p,SHsssNHp,SHtCH,SHdCH2,SHdsCH,SHaaCH,SHCHnX,SHCsats,SHCsatu,SHAvin,SHother,SHmisc,SsLi,SssBe,SssssBem,SsBH2,SssBH,SsssB,SssssBm,SsCH3,SdCH2,SssCH2,StCH,SdsCH,SaaCH,SsssCH,SddC,StsC,SdssC,SaasC,SaaaC,SssssC,SsNH3p,SsNH2,SssNH2p,SdNH,SssNH,SaaNH,StN,SsssNHp,SdsN,SaaN,SsssN,SddsN,SaasN,SssssNp,SsOH,SdO,SssO,SaaO,SaOm,SsOm,SsF,SsSiH3,SssSiH2,SsssSiH,SssssSi,SsPH2,SssPH,SsssP,SdsssP,SddsP,SsssssP,SsSH,SdS,SssS,SaaS,SdssS,SddssS,SssssssS,SSm,SsCl,SsGeH3,SssGeH2,SsssGeH,SssssGe,SsAsH2,SssAsH,SsssAs,SdsssAs,SddsAs,SsssssAs,SsSeH,SdSe,SssSe,SaaSe,SdssSe,SssssssSe,SddssSe,SsBr,SsSnH3,SssSnH2,SsssSnH,SssssSn,SsI,SsPbH3,SssPbH2,SsssPbH,SssssPb,minHBd,minwHBd,minHBa,minwHBa,minHBint2,minHBint3,minHBint4,minHBint5,minHBint6,minHBint7,minHBint8,minHBint9,minHBint10,minHsOH,minHdNH,minHsSH,minHsNH2,minHssNH,minHaaNH,minHsNH3p,minHssNH2p,minHsssNHp,minHtCH,minHdCH2,minHdsCH,minHaaCH,minHCHnX,minHCsats,minHCsatu,minHAvin,minHother,minHmisc,minsLi,minssBe,minssssBem,minsBH2,minssBH,minsssB,minssssBm,minsCH3,mindCH2,minssCH2,mintCH,mindsCH,minaaCH,minsssCH,minddC,mintsC,mindssC,minaasC,minaaaC,minssssC,minsNH3p,minsNH2,minssNH2p,mindNH,minssNH,minaaNH,mintN,minsssNHp,mindsN,minaaN,minsssN,minddsN,minaasN,minssssNp,minsOH,mindO,minssO,minaaO,minaOm,minsOm,minsF,minsSiH3,minssSiH2,minsssSiH,minssssSi,minsPH2,minssPH,minsssP,mindsssP,minddsP,minsssssP,minsSH,mindS,minssS,minaaS,mindssS,minddssS,minssssssS,minSm,minsCl,minsGeH3,minssGeH2,minsssGeH,minssssGe,minsAsH2,minssAsH,minsssAs,mindsssAs,minddsAs,minsssssAs,minsSeH,mindSe,minssSe,minaaSe,mindssSe,minssssssSe,minddssSe,minsBr,minsSnH3,minssSnH2,minsssSnH,minssssSn,minsI,minsPbH3,minssPbH2,minsssPbH,minssssPb,maxHBd,maxwHBd,maxHBa,maxwHBa,maxHBint2,maxHBint3,maxHBint4,maxHBint5,maxHBint6,maxHBint7,maxHBint8,maxHBint9,maxHBint10,maxHsOH,maxHdNH,maxHsSH,maxHsNH2,maxHssNH,maxHaaNH,maxHsNH3p,maxHssNH2p,maxHsssNHp,maxHtCH,maxHdCH2,maxHdsCH,maxHaaCH,maxHCHnX,maxHCsats,maxHCsatu,maxHAvin,maxHother,maxHmisc,maxsLi,maxssBe,maxssssBem,maxsBH2,maxssBH,maxsssB,maxssssBm,maxsCH3,maxdCH2,maxssCH2,maxtCH,maxdsCH,maxaaCH,maxsssCH,maxddC,maxtsC,maxdssC,maxaasC,maxaaaC,maxssssC,maxsNH3p,maxsNH2,maxssNH2p,maxdNH,maxssNH,maxaaNH,maxtN,maxsssNHp,maxdsN,maxaaN,maxsssN,maxddsN,maxaasN,maxssssNp,maxsOH,maxdO,maxssO,maxaaO,maxaOm,maxsOm,maxsF,maxsSiH3,maxssSiH2,maxsssSiH,maxssssSi,maxsPH2,maxssPH,maxsssP,maxdsssP,maxddsP,maxsssssP,maxsSH,maxdS,maxssS,maxaaS,maxdssS,maxddssS,maxssssssS,maxSm,maxsCl,maxsGeH3,maxssGeH2,maxsssGeH,maxssssGe,maxsAsH2,maxssAsH,maxsssAs,maxdsssAs,maxddsAs,maxsssssAs,maxsSeH,maxdSe,maxssSe,maxaaSe,maxdssSe,maxssssssSe,maxddssSe,maxsBr,maxsSnH3,maxssSnH2,maxsssSnH,maxssssSn,maxsI,maxsPbH3,maxssPbH2,maxsssPbH,maxssssPb,sumI,meanI,hmax,gmax,hmin,gmin,LipoaffinityIndex,MAXDN,MAXDP,DELS,MAXDN2,MAXDP2,DELS2,ETA_Alpha,ETA_AlphaP,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_Psi_1,ETA_dPsi_A,ETA_dPsi_B,ETA_Shape_P,ETA_Shape_Y,ETA_Shape_X,ETA_Beta,ETA_BetaP,ETA_Beta_s,ETA_BetaP_s,ETA_Beta_ns,ETA_BetaP_ns,ETA_dBeta,ETA_dBetaP,ETA_Beta_ns_d,ETA_BetaP_ns_d,ETA_Eta,ETA_EtaP,ETA_Eta_R,ETA_Eta_F,ETA_EtaP_F,ETA_Eta_L,ETA_EtaP_L,ETA_Eta_R_L,ETA_Eta_F_L,ETA_EtaP_F_L,ETA_Eta_B,ETA_EtaP_B,ETA_Eta_B_RC,ETA_EtaP_B_RC,FMF,fragC,nHBAcc,nHBAcc2,nHBAcc3,nHBAcc_Lipinski,nHBDon,nHBDon_Lipinski,HybRatio,IC0,IC1,IC2,IC3,IC4,IC5,TIC0,TIC1,TIC2,TIC3,TIC4,TIC5,SIC0,SIC1,SIC2,SIC3,SIC4,SIC5,CIC0,CIC1,CIC2,CIC3,CIC4,CIC5,BIC0,BIC1,BIC2,BIC3,BIC4,BIC5,MIC0,MIC1,MIC2,MIC3,MIC4,MIC5,ZMIC0,ZMIC1,ZMIC2,ZMIC3,ZMIC4,ZMIC5,Kier1,Kier2,Kier3,nAtomLC,nAtomP,nAtomLAC,MLogP,McGowan_Volume,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,MLFER_A,MLFER_BH,MLFER_BO,MLFER_S,MLFER_E,MLFER_L,MPC2,MPC3,MPC4,MPC5,MPC6,MPC7,MPC8,MPC9,MPC10,TPC,piPC1,piPC2,piPC3,piPC4,piPC5,piPC6,piPC7,piPC8,piPC9,piPC10,TpiPC,R_TpiPCTPC,PetitjeanNu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+UMLYSI0118,L,methylcyclopentadiene 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+UMLYSI0127,L,"1,2,3,4,4a,5,6,7,8,8a-decahydronaphthalene",C1CCC2CCCCC2C1,aromatic,C10H18,91-17-8,7044,UML,"stereochemistry not specifiied (see below) + + + +cis: C1C[C@H]2CCCC[C@H]2CC1 + + + +trans: 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+UMLYSI0251,L,tert-butyl propanoate,CCC(=O)OC(C)(C)C,saturated 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+UMLYSI0275,L,propyl 2-methylprop-2-enoate,CCCOC(=O)C(=C)C,unsaturated 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+UMLYSI0300,L,methyl (E)-pent-2-enoate,CC/C=C/C(=O)OC,unsaturated 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From c02df8676e33d83015e215b453cbd38d87bc4c29 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Wed, 14 Aug 2019 18:32:23 -0400 Subject: [PATCH 02/40] Update to links, author information --- README.md | 10 +++++----- setup.py | 9 ++++----- 2 files changed, 9 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index fa4b356..131381a 100644 --- a/README.md +++ b/README.md @@ -2,14 +2,14 @@ # ECNet: scalable, retrainable and deployable machine learning projects for fuel property prediction -[![GitHub version](https://badge.fury.io/gh/tjkessler%2FECNet.svg)](https://badge.fury.io/gh/tjkessler%2FECNet) +[![GitHub version](https://badge.fury.io/gh/ecrl%2FECNet.svg)](https://badge.fury.io/gh/ecrl%2FECNet) [![PyPI version](https://badge.fury.io/py/ecnet.svg)](https://badge.fury.io/py/ecnet) [![status](http://joss.theoj.org/papers/f556afbc97e18e1c1294d98e0f7ff99f/status.svg)](http://joss.theoj.org/papers/f556afbc97e18e1c1294d98e0f7ff99f) -[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/TJKessler/ECNet/master/LICENSE.txt) +[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/ECRL/ECNet/master/LICENSE.txt) [![Documentation Status](https://readthedocs.org/projects/ecnet/badge/?version=latest)](https://ecnet.readthedocs.io/en/latest/?badge=latest) [![Build Status](https://dev.azure.com/uml-ecrl/package-management/_apis/build/status/ECRL.ECNet?branchName=master)](https://dev.azure.com/uml-ecrl/package-management/_build/latest?definitionId=1&branchName=master) -**ECNet** is an open source Python package for creating scalable, retrainable and deployable machine learning projects with a focus on fuel property prediction. An ECNet __project__ is considered a collection of __pools__, where each pool contains a neural network that has been selected from a group of __candidate__ neural networks. Candidates are selected to represent pools based on their ability to optimize certain learning criteria (for example, performing optimially on unseen data). Each pool contributes a prediction derived from input data, and these predictions are averaged to calculate the project's final prediction. Using multiple pools allows a project to learn from a variety of learning and validation sets, which can reduce the project's prediction error. Projects can be saved and reused at a later time allowing additional training and deployable predictive models. +**ECNet** is an open source Python package for creating scalable, retrainable and deployable machine learning projects with a focus on fuel property prediction. An ECNet __project__ is considered a collection of __pools__, where each pool contains a neural network that has been selected from a group of __candidate__ neural networks. Candidates are selected to represent pools based on their ability to optimize certain learning criteria (for example, performing optimially on unseen data). Each pool contributes a prediction derived from input data, and these predictions are averaged to calculate the project's final prediction. Using multiple pools allows a project to learn from a variety of learning and validation sets, which can reduce the project's prediction error. Projects can be saved and reused at a later time allowing additional training and deployable predictive models. [T. Sennott et al.](https://doi.org/10.1115/ICEF2013-19185) have shown that neural networks can be applied to cetane number prediction with relatively little error. ECNet provides scientists an open source tool for predicting key fuel properties of potential next-generation biofuels, reducing the need for costly fuel synthesis and experimentation. @@ -17,7 +17,7 @@

-Using ECNet, [T. Kessler et al.](https://doi.org/10.1016/j.fuel.2017.06.015) have increased the generalizability of neural networks to predict the cetane number for a variety of molecular classes represented in our [cetane number database](https://github.com/TJKessler/ECNet/tree/master/databases), and have increased the accuracy of neural networks for predicting the cetane number of underrepresented molecular classes through targeted database expansion. +Using ECNet, [T. Kessler et al.](https://doi.org/10.1016/j.fuel.2017.06.015) have increased the generalizability of neural networks to predict the cetane number for a variety of molecular classes represented in our [cetane number database](https://github.com/ECRL/ECNet/tree/master/databases), and have increased the accuracy of neural networks for predicting the cetane number of underrepresented molecular classes through targeted database expansion. Future plans for ECNet include: - distributed candidate training for GPU's @@ -34,4 +34,4 @@ To contribute to ECNet, make a pull request. Contributions should include tests To report problems with the software or feature requests, file an issue. When reporting problems, include information such as error messages, your OS/environment and Python version. -For additional support/questions, contact Travis Kessler (travis.j.kessler@gmail.com), Hernan Gelaf-Romer (hernan_gelafromer@student.uml.edu) and/or John Hunter Mack (Hunter_Mack@uml.edu). +For additional support/questions, contact Travis Kessler (Travis_Kessler@student.uml.edu) and/or John Hunter Mack (Hunter_Mack@uml.edu). diff --git a/setup.py b/setup.py index 76539f4..75434fb 100644 --- a/setup.py +++ b/setup.py @@ -5,11 +5,10 @@ version='3.2.3', description='UMass Lowell Energy and Combustion Research Laboratory Neural' ' Network Software', - url='http://github.com/tjkessler/ecnet', - author='Travis Kessler, Hernan Gelaf-Romer, Sanskriti Sharma', - author_email='travis.j.kessler@gmail.com,' - ' Hernan_Gelafromer@student.uml.edu,' - ' Sanskriti_Sharma@student.uml.edu', + url='https://github.com/ecrl/ecnet', + author='Travis Kessler, John Hunter Mack', + author_email='Travis_Kessler@student.uml.edu,' + ' Hunter_Mack@uml.edu', license='MIT', packages=find_packages(), install_requires=[ From 2bf82e1f6ee1d4daf7c51be6a2b934596dcacbc9 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Wed, 14 Aug 2019 18:44:45 -0400 Subject: [PATCH 03/40] Force UTF-8 --- databases/ysi_database_v2.1.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/databases/ysi_database_v2.1.csv b/databases/ysi_database_v2.1.csv index d911f9f..07e0f33 100644 --- a/databases/ysi_database_v2.1.csv +++ b/databases/ysi_database_v2.1.csv @@ -533,7 +533,7 @@ UMLYSI0522,L,"4,5-dimethyl-(pentan-yl)-1,3-dioxolane",CCC(CC)C1OC(C)C(C)O1,,,,,, 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From 1db23b70c80fe1789f600c858d7d9dc395ce9ae6 Mon Sep 17 00:00:00 2001 From: Kessler Date: Thu, 15 Aug 2019 13:49:05 -0400 Subject: [PATCH 04/40] Validated SMILES strings, re-ran descriptor generation --- databases/ysi_database_v2.1.csv | 1136 +++++++++++++++---------------- 1 file changed, 568 insertions(+), 568 deletions(-) diff --git a/databases/ysi_database_v2.1.csv b/databases/ysi_database_v2.1.csv index 07e0f33..7740b10 100644 --- a/databases/ysi_database_v2.1.csv +++ b/databases/ysi_database_v2.1.csv @@ -1,568 +1,568 @@ 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By,Notes,TARGET,nAcid,ALogP,ALogp2,AMR,apol,naAromAtom,nAromBond,nAtom,nHeavyAtom,nH,nB,nC,nN,nO,nS,nP,nF,nCl,nBr,nI,nX,ATS0m,ATS1m,ATS2m,ATS3m,ATS4m,ATS5m,ATS6m,ATS7m,ATS8m,ATS0v,ATS1v,ATS2v,ATS3v,ATS4v,ATS5v,ATS6v,ATS7v,ATS8v,ATS0e,ATS1e,ATS2e,ATS3e,ATS4e,ATS5e,ATS6e,ATS7e,ATS8e,ATS0p,ATS1p,ATS2p,ATS3p,ATS4p,ATS5p,ATS6p,ATS7p,ATS8p,ATS0i,ATS1i,ATS2i,ATS3i,ATS4i,ATS5i,ATS6i,ATS7i,ATS8i,ATS0s,ATS1s,ATS2s,ATS3s,ATS4s,ATS5s,ATS6s,ATS7s,ATS8s,AATS0m,AATS1m,AATS2m,AATS3m,AATS4m,AATS5m,AATS6m,AATS7m,AATS8m,AATS0v,AATS1v,AATS2v,AATS3v,AATS4v,AATS5v,AATS6v,AATS7v,AATS8v,AATS0e,AATS1e,AATS2e,AATS3e,AATS4e,AATS5e,AATS6e,AATS7e,AATS8e,AATS0p,AATS1p,AATS2p,AATS3p,AATS4p,AATS5p,AATS6p,AATS7p,AATS8p,AATS0i,AATS1i,AATS2i,AATS3i,AATS4i,AATS5i,AATS6i,AATS7i,AATS8i,AATS0s,AATS1s,AATS2s,AATS3s,AATS4s,AATS5s,AATS6s,AATS7s,AATS8s,ATSC0c,ATSC1c,ATSC2c,ATSC3c,ATSC4c,ATSC5c,ATSC6c,ATSC7c,ATSC8c,ATSC0m,ATSC1m,ATSC2m,ATSC3m,ATSC4m,ATSC5m,ATSC6m,ATSC7m,ATSC8m,ATSC0v,ATSC1v,ATSC2v,ATSC3v,ATSC4v,ATSC5v,ATSC6v,ATSC7v,ATSC8v,ATSC0e,ATSC1e,ATSC2e,ATSC3e,ATSC4e,ATSC5e,ATSC6e,ATSC7e,ATSC8e,ATSC0p,ATSC1p,ATSC2p,ATSC3p,ATSC4p,ATSC5p,ATSC6p,ATSC7p,ATSC8p,ATSC0i,ATSC1i,ATSC2i,ATSC3i,ATSC4i,ATSC5i,ATSC6i,ATSC7i,ATSC8i,ATSC0s,ATSC1s,ATSC2s,ATSC3s,ATSC4s,ATSC5s,ATSC6s,ATSC7s,ATSC8s,AATSC0c,AATSC1c,AATSC2c,AATSC3c,AATSC4c,AATSC5c,AATSC6c,AATSC7c,AATSC8c,AATSC0m,AATSC1m,AATSC2m,AATSC3m,AATSC4m,AATSC5m,AATSC6m,AATSC7m,AATSC8m,AATSC0v,AATSC1v,AATSC2v,AATSC3v,AATSC4v,AATSC5v,AATSC6v,AATSC7v,AATSC8v,AATSC0e,AATSC1e,AATSC2e,AATSC3e,AATSC4e,AATSC5e,AATSC6e,AATSC7e,AATSC8e,AATSC0p,AATSC1p,AATSC2p,AATSC3p,AATSC4p,AATSC5p,AATSC6p,AATSC7p,AATSC8p,AATSC0i,AATSC1i,AATSC2i,AATSC3i,AATSC4i,AATSC5i,AATSC6i,AATSC7i,AATSC8i,AATSC0s,AATSC1s,AATSC2s,AATSC3s,AATSC4s,AATSC5s,AATSC6s,AATSC7s,AATSC8s,MATS1c,MATS2c,MATS3c,MATS4c,MATS5c,MATS6c,MATS7c,MATS8c,MATS1m,MATS2m,MATS3m,MATS4m,MATS5m,MATS6m,MATS7m,MATS8m,MATS1v,MATS2v,MATS3v,MATS4v,MATS5v,MATS6v,MATS7v,MATS8v,MATS1e,MATS2e,MATS3e,MATS4e,MATS5e,MATS6e,MATS7e,MATS8e,MATS1p,MATS2p,MATS3p,MATS4p,MATS5p,MATS6p,MATS7p,MATS8p,MATS1i,MATS2i,MATS3i,MATS4i,MATS5i,MATS6i,MATS7i,MATS8i,MATS1s,MATS2s,MATS3s,MATS4s,MATS5s,MATS6s,MATS7s,MATS8s,GATS1c,GATS2c,GATS3c,GATS4c,GATS5c,GATS6c,GATS7c,GATS8c,GATS1m,GATS2m,GATS3m,GATS4m,GATS5m,GATS6m,GATS7m,GATS8m,GATS1v,GATS2v,GATS3v,GATS4v,GATS5v,GATS6v,GATS7v,GATS8v,GATS1e,GATS2e,GATS3e,GATS4e,GATS5e,GATS6e,GATS7e,GATS8e,GATS1p,GATS2p,GATS3p,GATS4p,GATS5p,GATS6p,GATS7p,GATS8p,GATS1i,GATS2i,GATS3i,GATS4i,GATS5i,GATS6i,GATS7i,GATS8i,GATS1s,GATS2s,GATS3s,GATS4s,GATS5s,GATS6s,GATS7s,GATS8s,SpAbs_DzZ,SpMax_DzZ,SpDiam_DzZ,SpAD_DzZ,SpMAD_DzZ,EE_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,EE_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,EE_Dzv,SM1_Dzv,VE1_Dzv,VE2_Dzv,VE3_Dzv,VR1_Dzv,VR2_Dzv,VR3_Dzv,SpAbs_Dze,SpMax_Dze,SpDiam_Dze,SpAD_Dze,SpMAD_Dze,EE_Dze,SM1_Dze,VE1_Dze,VE2_Dze,VE3_Dze,VR1_Dze,VR2_Dze,VR3_Dze,SpAbs_Dzp,SpMax_Dzp,SpDiam_Dzp,SpAD_Dzp,SpMAD_Dzp,EE_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,EE_Dzi,SM1_Dzi,VE1_Dzi,VE2_Dzi,VE3_Dzi,VR1_Dzi,VR2_Dzi,VR3_Dzi,SpAbs_Dzs,SpMax_Dzs,SpDiam_Dzs,SpAD_Dzs,SpMAD_Dzs,EE_Dzs,SM1_Dzs,VE1_Dzs,VE2_Dzs,VE3_Dzs,VR1_Dzs,VR2_Dzs,VR3_Dzs,nBase,BCUTw-1l,BCUTw-1h,BCUTc-1l,BCUTc-1h,BCUTp-1l,BCUTp-1h,nBonds,nBonds2,nBondsS,nBondsS2,nBondsS3,nBondsD,nBondsD2,nBondsT,nBondsQ,nBondsM,bpol,SpMax1_Bhm,SpMax2_Bhm,SpMax3_Bhm,SpMax4_Bhm,SpMax5_Bhm,SpMax6_Bhm,SpMax7_Bhm,SpMax8_Bhm,SpMin1_Bhm,SpMin2_Bhm,SpMin3_Bhm,SpMin4_Bhm,SpMin5_Bhm,SpMin6_Bhm,SpMin7_Bhm,SpMin8_Bhm,SpMax1_Bhv,SpMax2_Bhv,SpMax3_Bhv,SpMax4_Bhv,SpMax5_Bhv,SpMax6_Bhv,SpMax7_Bhv,SpMax8_Bhv,SpMin1_Bhv,SpMin2_Bhv,SpMin3_Bhv,SpMin4_Bhv,SpMin5_Bhv,SpMin6_Bhv,SpMin7_Bhv,SpMin8_Bhv,SpMax1_Bhe,SpMax2_Bhe,SpMax3_Bhe,SpMax4_Bhe,SpMax5_Bhe,SpMax6_Bhe,SpMax7_Bhe,SpMax8_Bhe,SpMin1_Bhe,SpMin2_Bhe,SpMin3_Bhe,SpMin4_Bhe,SpMin5_Bhe,SpMin6_Bhe,SpMin7_Bhe,SpMin8_Bhe,SpMax1_Bhp,SpMax2_Bhp,SpMax3_Bhp,SpMax4_Bhp,SpMax5_Bhp,SpMax6_Bhp,SpMax7_Bhp,SpMax8_Bhp,SpMin1_Bhp,SpMin2_Bhp,SpMin3_Bhp,SpMin4_Bhp,SpMin5_Bhp,SpMin6_Bhp,SpMin7_Bhp,SpMin8_Bhp,SpMax1_Bhi,SpMax2_Bhi,SpMax3_Bhi,SpMax4_Bhi,SpMax5_Bhi,SpMax6_Bhi,SpMax7_Bhi,SpMax8_Bhi,SpMin1_Bhi,SpMin2_Bhi,SpMin3_Bhi,SpMin4_Bhi,SpMin5_Bhi,SpMin6_Bhi,SpMin7_Bhi,SpMin8_Bhi,SpMax1_Bhs,SpMax2_Bhs,SpMax3_Bhs,SpMax4_Bhs,SpMax5_Bhs,SpMax6_Bhs,SpMax7_Bhs,SpMax8_Bhs,SpMin1_Bhs,SpMin2_Bhs,SpMin3_Bhs,SpMin4_Bhs,SpMin5_Bhs,SpMin6_Bhs,SpMin7_Bhs,SpMin8_Bhs,C1SP1,C2SP1,C1SP2,C2SP2,C3SP2,C1SP3,C2SP3,C3SP3,C4SP3,SCH-3,SCH-4,SCH-5,SCH-6,SCH-7,VCH-3,VCH-4,VCH-5,VCH-6,VCH-7,SC-3,SC-4,SC-5,SC-6,VC-3,VC-4,VC-5,VC-6,SPC-4,SPC-5,SPC-6,VPC-4,VPC-5,VPC-6,SP-0,SP-1,SP-2,SP-3,SP-4,SP-5,SP-6,SP-7,ASP-0,ASP-1,ASP-2,ASP-3,ASP-4,ASP-5,ASP-6,ASP-7,VP-0,VP-1,VP-2,VP-3,VP-4,VP-5,VP-6,VP-7,AVP-0,AVP-1,AVP-2,AVP-3,AVP-4,AVP-5,AVP-6,AVP-7,Sv,Sse,Spe,Sare,Sp,Si,Mv,Mse,Mpe,Mare,Mp,Mi,CrippenLogP,CrippenMR,SpMax_Dt,SpDiam_Dt,SpAD_Dt,SpMAD_Dt,EE_Dt,VE1_Dt,VE2_Dt,VE3_Dt,VR1_Dt,VR2_Dt,VR3_Dt,ECCEN,nHBd,nwHBd,nHBa,nwHBa,nHBint2,nHBint3,nHBint4,nHBint5,nHBint6,nHBint7,nHBint8,nHBint9,nHBint10,nHsOH,nHdNH,nHsSH,nHsNH2,nHssNH,nHaaNH,nHsNH3p,nHssNH2p,nHsssNHp,nHtCH,nHdCH2,nHdsCH,nHaaCH,nHCHnX,nHCsats,nHCsatu,nHAvin,nHother,nHmisc,nsLi,nssBe,nssssBem,nsBH2,nssBH,nsssB,nssssBm,nsCH3,ndCH2,nssCH2,ntCH,ndsCH,naaCH,nsssCH,nddC,ntsC,ndssC,naasC,naaaC,nssssC,nsNH3p,nsNH2,nssNH2p,ndNH,nssNH,naaNH,ntN,nsssNHp,ndsN,naaN,nsssN,nddsN,naasN,nssssNp,nsOH,ndO,nssO,naaO,naOm,nsOm,nsF,nsSiH3,nssSiH2,nsssSiH,nssssSi,nsPH2,nssPH,nsssP,ndsssP,nddsP,nsssssP,nsSH,ndS,nssS,naaS,ndssS,nddssS,nssssssS,nSm,nsCl,nsGeH3,nssGeH2,nsssGeH,nssssGe,nsAsH2,nssAsH,nsssAs,ndsssAs,nddsAs,nsssssAs,nsSeH,ndSe,nssSe,naaSe,ndssSe,nssssssSe,nddssSe,nsBr,nsSnH3,nssSnH2,nsssSnH,nssssSn,nsI,nsPbH3,nssPbH2,nsssPbH,nssssPb,SHBd,SwHBd,SHBa,SwHBa,SHBint2,SHBint3,SHBint4,SHBint5,SHBint6,SHBint7,SHBint8,SHBint9,SHBint10,SHsOH,SHdNH,SHsSH,SHsNH2,SHssNH,SHaaNH,SHsNH3p,SHssNH2p,SHsssNHp,SHtCH,SHdCH2,SHdsCH,SHaaCH,SHCHnX,SHCsats,SHCsatu,SHAvin,SHother,SHmisc,SsLi,SssBe,SssssBem,SsBH2,SssBH,SsssB,SssssBm,SsCH3,SdCH2,SssCH2,StCH,SdsCH,SaaCH,SsssCH,SddC,StsC,SdssC,SaasC,SaaaC,SssssC,SsNH3p,SsNH2,SssNH2p,SdNH,SssNH,SaaNH,StN,SsssNHp,SdsN,SaaN,SsssN,SddsN,SaasN,SssssNp,SsOH,SdO,SssO,SaaO,SaOm,SsOm,SsF,SsSiH3,SssSiH2,SsssSiH,SssssSi,SsPH2,SssPH,SsssP,SdsssP,SddsP,SsssssP,SsSH,SdS,SssS,SaaS,SdssS,SddssS,SssssssS,SSm,SsCl,SsGeH3,SssGeH2,SsssGeH,SssssGe,SsAsH2,SssAsH,SsssAs,SdsssAs,SddsAs,SsssssAs,SsSeH,SdSe,SssSe,SaaSe,SdssSe,SssssssSe,SddssSe,SsBr,SsSnH3,SssSnH2,SsssSnH,SssssSn,SsI,SsPbH3,SssPbH2,SsssPbH,SssssPb,minHBd,minwHBd,minHBa,minwHBa,minHBint2,minHBint3,minHBint4,minHBint5,minHBint6,minHBint7,minHBint8,minHBint9,minHBint10,minHsOH,minHdNH,minHsSH,minHsNH2,minHssNH,minHaaNH,minHsNH3p,minHssNH2p,minHsssNHp,minHtCH,minHdCH2,minHdsCH,minHaaCH,minHCHnX,minHCsats,minHCsatu,minHAvin,minHother,minHmisc,minsLi,minssBe,minssssBem,minsBH2,minssBH,minsssB,minssssBm,minsCH3,mindCH2,minssCH2,mintCH,mindsCH,minaaCH,minsssCH,minddC,mintsC,mindssC,minaasC,minaaaC,minssssC,minsNH3p,minsNH2,minssNH2p,mindNH,minssNH,minaaNH,mintN,minsssNHp,mindsN,minaaN,minsssN,minddsN,minaasN,minssssNp,minsOH,mindO,minssO,minaaO,minaOm,minsOm,minsF,minsSiH3,minssSiH2,minsssSiH,minssssSi,minsPH2,minssPH,minsssP,mindsssP,minddsP,minsssssP,minsSH,mindS,minssS,minaaS,mindssS,minddssS,minssssssS,minSm,minsCl,minsGeH3,minssGeH2,minsssGeH,minssssGe,minsAsH2,minssAsH,minsssAs,mindsssAs,minddsAs,minsssssAs,minsSeH,mindSe,minssSe,minaaSe,mindssSe,minssssssSe,minddssSe,minsBr,minsSnH3,minssSnH2,minsssSnH,minssssSn,minsI,minsPbH3,minssPbH2,minsssPbH,minssssPb,maxHBd,maxwHBd,maxHBa,maxwHBa,maxHBint2,maxHBint3,maxHBint4,maxHBint5,maxHBint6,maxHBint7,maxHBint8,maxHBint9,maxHBint10,maxHsOH,maxHdNH,maxHsSH,maxHsNH2,maxHssNH,maxHaaNH,maxHsNH3p,maxHssNH2p,maxHsssNHp,maxHtCH,maxHdCH2,maxHdsCH,maxHaaCH,maxHCHnX,maxHCsats,maxHCsatu,maxHAvin,maxHother,maxHmisc,maxsLi,maxssBe,maxssssBem,maxsBH2,maxssBH,maxsssB,maxssssBm,maxsCH3,maxdCH2,maxssCH2,maxtCH,maxdsCH,maxaaCH,maxsssCH,maxddC,maxtsC,maxdssC,maxaasC,maxaaaC,maxssssC,maxsNH3p,maxsNH2,maxssNH2p,maxdNH,maxssNH,maxaaNH,maxtN,maxsssNHp,maxdsN,maxaaN,maxsssN,maxddsN,maxaasN,maxssssNp,maxsOH,maxdO,maxssO,maxaaO,maxaOm,maxsOm,maxsF,maxsSiH3,maxssSiH2,maxsssSiH,maxssssSi,maxsPH2,maxssPH,maxsssP,maxdsssP,maxddsP,maxsssssP,maxsSH,maxdS,maxssS,maxaaS,maxdssS,maxddssS,maxssssssS,maxSm,maxsCl,maxsGeH3,maxssGeH2,maxsssGeH,maxssssGe,maxsAsH2,maxssAsH,maxsssAs,maxdsssAs,maxddsAs,maxsssssAs,maxsSeH,maxdSe,maxssSe,maxaaSe,maxdssSe,maxssssssSe,maxddssSe,maxsBr,maxsSnH3,maxssSnH2,maxsssSnH,maxssssSn,maxsI,maxsPbH3,maxssPbH2,maxsssPbH,maxssssPb,sumI,meanI,hmax,gmax,hmin,gmin,LipoaffinityIndex,MAXDN,MAXDP,DELS,MAXDN2,MAXDP2,DELS2,ETA_Alpha,ETA_AlphaP,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_Psi_1,ETA_dPsi_A,ETA_dPsi_B,ETA_Shape_P,ETA_Shape_Y,ETA_Shape_X,ETA_Beta,ETA_BetaP,ETA_Beta_s,ETA_BetaP_s,ETA_Beta_ns,ETA_BetaP_ns,ETA_dBeta,ETA_dBetaP,ETA_Beta_ns_d,ETA_BetaP_ns_d,ETA_Eta,ETA_EtaP,ETA_Eta_R,ETA_Eta_F,ETA_EtaP_F,ETA_Eta_L,ETA_EtaP_L,ETA_Eta_R_L,ETA_Eta_F_L,ETA_EtaP_F_L,ETA_Eta_B,ETA_EtaP_B,ETA_Eta_B_RC,ETA_EtaP_B_RC,FMF,fragC,nHBAcc,nHBAcc2,nHBAcc3,nHBAcc_Lipinski,nHBDon,nHBDon_Lipinski,HybRatio,IC0,IC1,IC2,IC3,IC4,IC5,TIC0,TIC1,TIC2,TIC3,TIC4,TIC5,SIC0,SIC1,SIC2,SIC3,SIC4,SIC5,CIC0,CIC1,CIC2,CIC3,CIC4,CIC5,BIC0,BIC1,BIC2,BIC3,BIC4,BIC5,MIC0,MIC1,MIC2,MIC3,MIC4,MIC5,ZMIC0,ZMIC1,ZMIC2,ZMIC3,ZMIC4,ZMIC5,Kier1,Kier2,Kier3,nAtomLC,nAtomP,nAtomLAC,MLogP,McGowan_Volume,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,MLFER_A,MLFER_BH,MLFER_BO,MLFER_S,MLFER_E,MLFER_L,MPC2,MPC3,MPC4,MPC5,MPC6,MPC7,MPC8,MPC9,MPC10,TPC,piPC1,piPC2,piPC3,piPC4,piPC5,piPC6,piPC7,piPC8,piPC9,piPC10,TpiPC,R_TpiPCTPC,PetitjeanNu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-UMLYSI0118,L,methylcyclopentadiene 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-UMLYSI0127,L,"1,2,3,4,4a,5,6,7,8,8a-decahydronaphthalene",C1CCC2CCCCC2C1,aromatic,C10H18,91-17-8,7044,UML,"stereochemistry not specifiied (see below) - - - -cis: C1C[C@H]2CCCC[C@H]2CC1 - - - -trans: 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-UMLYSI0223,L,3-methylbutyl acetate,CC(C)CCOC(=O)C,saturated 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-UMLYSI0228,L,butyl propanoate,CCCCOC(=O)CC,saturated 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-UMLYSI0240,L,methyl (E)-2-methylpent-2-enoate,CC/C=C(\C)/C(=O)OC,unsaturated 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-UMLYSI0251,L,tert-butyl propanoate,CCC(=O)OC(C)(C)C,saturated 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-UMLYSI0260,L,methyl (E)-pent-3-enoate,C/C=C/CC(=O)OC,unsaturated 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-UMLYSI0269,L,methyl (E)-2-methylbut-2-enoate,C/C=C(\C)/C(=O)OC,unsaturated 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-UMLYSI0275,L,propyl 2-methylprop-2-enoate,CCCOC(=O)C(=C)C,unsaturated 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-UMLYSI0279,L,methyl 3-methylbut-2-enoate,CC(=CC(=O)OC)C,unsaturated 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-UMLYSI0283,L,butyl acetate,CCCCOC(=O)C,saturated 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-UMLYSI0286,L,methyl (E)-hex-2-enoate,CCC/C=C/C(=O)OC,unsaturated 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-UMLYSI0297,L,ethyl 2-methylbut-2-enoate,CCOC(=O)C(=CC)C,unsaturated 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-UMLYSI0300,L,methyl (E)-pent-2-enoate,CC/C=C/C(=O)OC,unsaturated 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-UMLYSI0329,L,methyl 2-methylprop-2-enoate,CC(=C)C(=O)OC,unsaturated 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-UMLYSI0347,L,ethyl 2-methylpropanoate,CCOC(=O)C(C)C,saturated 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-UMLYSI0358,L,methyl 2-methylbutanoate,CCC(C)C(=O)OC,saturated 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-UMLYSI0367,L,methyl 2-methylpropanoate,CC(C)C(=O)OC,saturated 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-UMLYSI0386,L,methyl butanoate,CCCC(=O)OC,saturated 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-UMLYSI0408,L,ethyl acetate,CCOC(=O)C,saturated 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-UMLYSI0441,L,ethyl (E)-2-methylbut-2-enoate,CCOC(=O)/C(=C/C)/C,unsaturated 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-UMLYSI0556,L,methyl 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By,Notes,TARGET,nAcid,ALogP,ALogp2,AMR,apol,naAromAtom,nAromBond,nAtom,nHeavyAtom,nH,nB,nC,nN,nO,nS,nP,nF,nCl,nBr,nI,nX,ATS0m,ATS1m,ATS2m,ATS3m,ATS4m,ATS5m,ATS6m,ATS7m,ATS8m,ATS0v,ATS1v,ATS2v,ATS3v,ATS4v,ATS5v,ATS6v,ATS7v,ATS8v,ATS0e,ATS1e,ATS2e,ATS3e,ATS4e,ATS5e,ATS6e,ATS7e,ATS8e,ATS0p,ATS1p,ATS2p,ATS3p,ATS4p,ATS5p,ATS6p,ATS7p,ATS8p,ATS0i,ATS1i,ATS2i,ATS3i,ATS4i,ATS5i,ATS6i,ATS7i,ATS8i,ATS0s,ATS1s,ATS2s,ATS3s,ATS4s,ATS5s,ATS6s,ATS7s,ATS8s,AATS0m,AATS1m,AATS2m,AATS3m,AATS4m,AATS5m,AATS6m,AATS7m,AATS8m,AATS0v,AATS1v,AATS2v,AATS3v,AATS4v,AATS5v,AATS6v,AATS7v,AATS8v,AATS0e,AATS1e,AATS2e,AATS3e,AATS4e,AATS5e,AATS6e,AATS7e,AATS8e,AATS0p,AATS1p,AATS2p,AATS3p,AATS4p,AATS5p,AATS6p,AATS7p,AATS8p,AATS0i,AATS1i,AATS2i,AATS3i,AATS4i,AATS5i,AATS6i,AATS7i,AATS8i,AATS0s,AATS1s,AATS2s,AATS3s,AATS4s,AATS5s,AATS6s,AATS7s,AATS8s,ATSC0c,ATSC1c,ATSC2c,ATSC3c,ATSC4c,ATSC5c,ATSC6c,ATSC7c,ATSC8c,ATSC0m,ATSC1m,ATSC2m,ATSC3m,ATSC4m,ATSC5m,ATSC6m,ATSC7m,ATSC8m,ATSC0v,ATSC1v,ATSC2v,ATSC3v,ATSC4v,ATSC5v,ATSC6v,ATSC7v,ATSC8v,ATSC0e,ATSC1e,ATSC2e,ATSC3e,ATSC4e,ATSC5e,ATSC6e,ATSC7e,ATSC8e,ATSC0p,ATSC1p,ATSC2p,ATSC3p,ATSC4p,ATSC5p,ATSC6p,ATSC7p,ATSC8p,ATSC0i,ATSC1i,ATSC2i,ATSC3i,ATSC4i,ATSC5i,ATSC6i,ATSC7i,ATSC8i,ATSC0s,ATSC1s,ATSC2s,ATSC3s,ATSC4s,ATSC5s,ATSC6s,ATSC7s,ATSC8s,AATSC0c,AATSC1c,AATSC2c,AATSC3c,AATSC4c,AATSC5c,AATSC6c,AATSC7c,AATSC8c,AATSC0m,AATSC1m,AATSC2m,AATSC3m,AATSC4m,AATSC5m,AATSC6m,AATSC7m,AATSC8m,AATSC0v,AATSC1v,AATSC2v,AATSC3v,AATSC4v,AATSC5v,AATSC6v,AATSC7v,AATSC8v,AATSC0e,AATSC1e,AATSC2e,AATSC3e,AATSC4e,AATSC5e,AATSC6e,AATSC7e,AATSC8e,AATSC0p,AATSC1p,AATSC2p,AATSC3p,AATSC4p,AATSC5p,AATSC6p,AATSC7p,AATSC8p,AATSC0i,AATSC1i,AATSC2i,AATSC3i,AATSC4i,AATSC5i,AATSC6i,AATSC7i,AATSC8i,AATSC0s,AATSC1s,AATSC2s,AATSC3s,AATSC4s,AATSC5s,AATSC6s,AATSC7s,AATSC8s,MATS1c,MATS2c,MATS3c,MATS4c,MATS5c,MATS6c,MATS7c,MATS8c,MATS1m,MATS2m,MATS3m,MATS4m,MATS5m,MATS6m,MATS7m,MATS8m,MATS1v,MATS2v,MATS3v,MATS4v,MATS5v,MATS6v,MATS7v,MATS8v,MATS1e,MATS2e,MATS3e,MATS4e,MATS5e,MATS6e,MATS7e,MATS8e,MATS1p,MATS2p,MATS3p,MATS4p,MATS5p,MATS6p,MATS7p,MATS8p,MATS1i,MATS2i,MATS3i,MATS4i,MATS5i,MATS6i,MATS7i,MATS8i,MATS1s,MATS2s,MATS3s,MATS4s,MATS5s,MATS6s,MATS7s,MATS8s,GATS1c,GATS2c,GATS3c,GATS4c,GATS5c,GATS6c,GATS7c,GATS8c,GATS1m,GATS2m,GATS3m,GATS4m,GATS5m,GATS6m,GATS7m,GATS8m,GATS1v,GATS2v,GATS3v,GATS4v,GATS5v,GATS6v,GATS7v,GATS8v,GATS1e,GATS2e,GATS3e,GATS4e,GATS5e,GATS6e,GATS7e,GATS8e,GATS1p,GATS2p,GATS3p,GATS4p,GATS5p,GATS6p,GATS7p,GATS8p,GATS1i,GATS2i,GATS3i,GATS4i,GATS5i,GATS6i,GATS7i,GATS8i,GATS1s,GATS2s,GATS3s,GATS4s,GATS5s,GATS6s,GATS7s,GATS8s,SpAbs_DzZ,SpMax_DzZ,SpDiam_DzZ,SpAD_DzZ,SpMAD_DzZ,EE_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,EE_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,EE_Dzv,SM1_Dzv,VE1_Dzv,VE2_Dzv,VE3_Dzv,VR1_Dzv,VR2_Dzv,VR3_Dzv,SpAbs_Dze,SpMax_Dze,SpDiam_Dze,SpAD_Dze,SpMAD_Dze,EE_Dze,SM1_Dze,VE1_Dze,VE2_Dze,VE3_Dze,VR1_Dze,VR2_Dze,VR3_Dze,SpAbs_Dzp,SpMax_Dzp,SpDiam_Dzp,SpAD_Dzp,SpMAD_Dzp,EE_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,EE_Dzi,SM1_Dzi,VE1_Dzi,VE2_Dzi,VE3_Dzi,VR1_Dzi,VR2_Dzi,VR3_Dzi,SpAbs_Dzs,SpMax_Dzs,SpDiam_Dzs,SpAD_Dzs,SpMAD_Dzs,EE_Dzs,SM1_Dzs,VE1_Dzs,VE2_Dzs,VE3_Dzs,VR1_Dzs,VR2_Dzs,VR3_Dzs,nBase,BCUTw-1l,BCUTw-1h,BCUTc-1l,BCUTc-1h,BCUTp-1l,BCUTp-1h,nBonds,nBonds2,nBondsS,nBondsS2,nBondsS3,nBondsD,nBondsD2,nBondsT,nBondsQ,nBondsM,bpol,SpMax1_Bhm,SpMax2_Bhm,SpMax3_Bhm,SpMax4_Bhm,SpMax5_Bhm,SpMax6_Bhm,SpMax7_Bhm,SpMax8_Bhm,SpMin1_Bhm,SpMin2_Bhm,SpMin3_Bhm,SpMin4_Bhm,SpMin5_Bhm,SpMin6_Bhm,SpMin7_Bhm,SpMin8_Bhm,SpMax1_Bhv,SpMax2_Bhv,SpMax3_Bhv,SpMax4_Bhv,SpMax5_Bhv,SpMax6_Bhv,SpMax7_Bhv,SpMax8_Bhv,SpMin1_Bhv,SpMin2_Bhv,SpMin3_Bhv,SpMin4_Bhv,SpMin5_Bhv,SpMin6_Bhv,SpMin7_Bhv,SpMin8_Bhv,SpMax1_Bhe,SpMax2_Bhe,SpMax3_Bhe,SpMax4_Bhe,SpMax5_Bhe,SpMax6_Bhe,SpMax7_Bhe,SpMax8_Bhe,SpMin1_Bhe,SpMin2_Bhe,SpMin3_Bhe,SpMin4_Bhe,SpMin5_Bhe,SpMin6_Bhe,SpMin7_Bhe,SpMin8_Bhe,SpMax1_Bhp,SpMax2_Bhp,SpMax3_Bhp,SpMax4_Bhp,SpMax5_Bhp,SpMax6_Bhp,SpMax7_Bhp,SpMax8_Bhp,SpMin1_Bhp,SpMin2_Bhp,SpMin3_Bhp,SpMin4_Bhp,SpMin5_Bhp,SpMin6_Bhp,SpMin7_Bhp,SpMin8_Bhp,SpMax1_Bhi,SpMax2_Bhi,SpMax3_Bhi,SpMax4_Bhi,SpMax5_Bhi,SpMax6_Bhi,SpMax7_Bhi,SpMax8_Bhi,SpMin1_Bhi,SpMin2_Bhi,SpMin3_Bhi,SpMin4_Bhi,SpMin5_Bhi,SpMin6_Bhi,SpMin7_Bhi,SpMin8_Bhi,SpMax1_Bhs,SpMax2_Bhs,SpMax3_Bhs,SpMax4_Bhs,SpMax5_Bhs,SpMax6_Bhs,SpMax7_Bhs,SpMax8_Bhs,SpMin1_Bhs,SpMin2_Bhs,SpMin3_Bhs,SpMin4_Bhs,SpMin5_Bhs,SpMin6_Bhs,SpMin7_Bhs,SpMin8_Bhs,C1SP1,C2SP1,C1SP2,C2SP2,C3SP2,C1SP3,C2SP3,C3SP3,C4SP3,SCH-3,SCH-4,SCH-5,SCH-6,SCH-7,VCH-3,VCH-4,VCH-5,VCH-6,VCH-7,SC-3,SC-4,SC-5,SC-6,VC-3,VC-4,VC-5,VC-6,SPC-4,SPC-5,SPC-6,VPC-4,VPC-5,VPC-6,SP-0,SP-1,SP-2,SP-3,SP-4,SP-5,SP-6,SP-7,ASP-0,ASP-1,ASP-2,ASP-3,ASP-4,ASP-5,ASP-6,ASP-7,VP-0,VP-1,VP-2,VP-3,VP-4,VP-5,VP-6,VP-7,AVP-0,AVP-1,AVP-2,AVP-3,AVP-4,AVP-5,AVP-6,AVP-7,Sv,Sse,Spe,Sare,Sp,Si,Mv,Mse,Mpe,Mare,Mp,Mi,CrippenLogP,CrippenMR,SpMax_Dt,SpDiam_Dt,SpAD_Dt,SpMAD_Dt,EE_Dt,VE1_Dt,VE2_Dt,VE3_Dt,VR1_Dt,VR2_Dt,VR3_Dt,ECCEN,nHBd,nwHBd,nHBa,nwHBa,nHBint2,nHBint3,nHBint4,nHBint5,nHBint6,nHBint7,nHBint8,nHBint9,nHBint10,nHsOH,nHdNH,nHsSH,nHsNH2,nHssNH,nHaaNH,nHsNH3p,nHssNH2p,nHsssNHp,nHtCH,nHdCH2,nHdsCH,nHaaCH,nHCHnX,nHCsats,nHCsatu,nHAvin,nHother,nHmisc,nsLi,nssBe,nssssBem,nsBH2,nssBH,nsssB,nssssBm,nsCH3,ndCH2,nssCH2,ntCH,ndsCH,naaCH,nsssCH,nddC,ntsC,ndssC,naasC,naaaC,nssssC,nsNH3p,nsNH2,nssNH2p,ndNH,nssNH,naaNH,ntN,nsssNHp,ndsN,naaN,nsssN,nddsN,naasN,nssssNp,nsOH,ndO,nssO,naaO,naOm,nsOm,nsF,nsSiH3,nssSiH2,nsssSiH,nssssSi,nsPH2,nssPH,nsssP,ndsssP,nddsP,nsssssP,nsSH,ndS,nssS,naaS,ndssS,nddssS,nssssssS,nSm,nsCl,nsGeH3,nssGeH2,nsssGeH,nssssGe,nsAsH2,nssAsH,nsssAs,ndsssAs,nddsAs,nsssssAs,nsSeH,ndSe,nssSe,naaSe,ndssSe,nssssssSe,nddssSe,nsBr,nsSnH3,nssSnH2,nsssSnH,nssssSn,nsI,nsPbH3,nssPbH2,nsssPbH,nssssPb,SHBd,SwHBd,SHBa,SwHBa,SHBint2,SHBint3,SHBint4,SHBint5,SHBint6,SHBint7,SHBint8,SHBint9,SHBint10,SHsOH,SHdNH,SHsSH,SHsNH2,SHssNH,SHaaNH,SHsNH3p,SHssNH2p,SHsssNHp,SHtCH,SHdCH2,SHdsCH,SHaaCH,SHCHnX,SHCsats,SHCsatu,SHAvin,SHother,SHmisc,SsLi,SssBe,SssssBem,SsBH2,SssBH,SsssB,SssssBm,SsCH3,SdCH2,SssCH2,StCH,SdsCH,SaaCH,SsssCH,SddC,StsC,SdssC,SaasC,SaaaC,SssssC,SsNH3p,SsNH2,SssNH2p,SdNH,SssNH,SaaNH,StN,SsssNHp,SdsN,SaaN,SsssN,SddsN,SaasN,SssssNp,SsOH,SdO,SssO,SaaO,SaOm,SsOm,SsF,SsSiH3,SssSiH2,SsssSiH,SssssSi,SsPH2,SssPH,SsssP,SdsssP,SddsP,SsssssP,SsSH,SdS,SssS,SaaS,SdssS,SddssS,SssssssS,SSm,SsCl,SsGeH3,SssGeH2,SsssGeH,SssssGe,SsAsH2,SssAsH,SsssAs,SdsssAs,SddsAs,SsssssAs,SsSeH,SdSe,SssSe,SaaSe,SdssSe,SssssssSe,SddssSe,SsBr,SsSnH3,SssSnH2,SsssSnH,SssssSn,SsI,SsPbH3,SssPbH2,SsssPbH,SssssPb,minHBd,minwHBd,minHBa,minwHBa,minHBint2,minHBint3,minHBint4,minHBint5,minHBint6,minHBint7,minHBint8,minHBint9,minHBint10,minHsOH,minHdNH,minHsSH,minHsNH2,minHssNH,minHaaNH,minHsNH3p,minHssNH2p,minHsssNHp,minHtCH,minHdCH2,minHdsCH,minHaaCH,minHCHnX,minHCsats,minHCsatu,minHAvin,minHother,minHmisc,minsLi,minssBe,minssssBem,minsBH2,minssBH,minsssB,minssssBm,minsCH3,mindCH2,minssCH2,mintCH,mindsCH,minaaCH,minsssCH,minddC,mintsC,mindssC,minaasC,minaaaC,minssssC,minsNH3p,minsNH2,minssNH2p,mindNH,minssNH,minaaNH,mintN,minsssNHp,mindsN,minaaN,minsssN,minddsN,minaasN,minssssNp,minsOH,mindO,minssO,minaaO,minaOm,minsOm,minsF,minsSiH3,minssSiH2,minsssSiH,minssssSi,minsPH2,minssPH,minsssP,mindsssP,minddsP,minsssssP,minsSH,mindS,minssS,minaaS,mindssS,minddssS,minssssssS,minSm,minsCl,minsGeH3,minssGeH2,minsssGeH,minssssGe,minsAsH2,minssAsH,minsssAs,mindsssAs,minddsAs,minsssssAs,minsSeH,mindSe,minssSe,minaaSe,mindssSe,minssssssSe,minddssSe,minsBr,minsSnH3,minssSnH2,minsssSnH,minssssSn,minsI,minsPbH3,minssPbH2,minsssPbH,minssssPb,maxHBd,maxwHBd,maxHBa,maxwHBa,maxHBint2,maxHBint3,maxHBint4,maxHBint5,maxHBint6,maxHBint7,maxHBint8,maxHBint9,maxHBint10,maxHsOH,maxHdNH,maxHsSH,maxHsNH2,maxHssNH,maxHaaNH,maxHsNH3p,maxHssNH2p,maxHsssNHp,maxHtCH,maxHdCH2,maxHdsCH,maxHaaCH,maxHCHnX,maxHCsats,maxHCsatu,maxHAvin,maxHother,maxHmisc,maxsLi,maxssBe,maxssssBem,maxsBH2,maxssBH,maxsssB,maxssssBm,maxsCH3,maxdCH2,maxssCH2,maxtCH,maxdsCH,maxaaCH,maxsssCH,maxddC,maxtsC,maxdssC,maxaasC,maxaaaC,maxssssC,maxsNH3p,maxsNH2,maxssNH2p,maxdNH,maxssNH,maxaaNH,maxtN,maxsssNHp,maxdsN,maxaaN,maxsssN,maxddsN,maxaasN,maxssssNp,maxsOH,maxdO,maxssO,maxaaO,maxaOm,maxsOm,maxsF,maxsSiH3,maxssSiH2,maxsssSiH,maxssssSi,maxsPH2,maxssPH,maxsssP,maxdsssP,maxddsP,maxsssssP,maxsSH,maxdS,maxssS,maxaaS,maxdssS,maxddssS,maxssssssS,maxSm,maxsCl,maxsGeH3,maxssGeH2,maxsssGeH,maxssssGe,maxsAsH2,maxssAsH,maxsssAs,maxdsssAs,maxddsAs,maxsssssAs,maxsSeH,maxdSe,maxssSe,maxaaSe,maxdssSe,maxssssssSe,maxddssSe,maxsBr,maxsSnH3,maxssSnH2,maxsssSnH,maxssssSn,maxsI,maxsPbH3,maxssPbH2,maxsssPbH,maxssssPb,sumI,meanI,hmax,gmax,hmin,gmin,LipoaffinityIndex,MAXDN,MAXDP,DELS,MAXDN2,MAXDP2,DELS2,ETA_Alpha,ETA_AlphaP,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_Psi_1,ETA_dPsi_A,ETA_dPsi_B,ETA_Shape_P,ETA_Shape_Y,ETA_Shape_X,ETA_Beta,ETA_BetaP,ETA_Beta_s,ETA_BetaP_s,ETA_Beta_ns,ETA_BetaP_ns,ETA_dBeta,ETA_dBetaP,ETA_Beta_ns_d,ETA_BetaP_ns_d,ETA_Eta,ETA_EtaP,ETA_Eta_R,ETA_Eta_F,ETA_EtaP_F,ETA_Eta_L,ETA_EtaP_L,ETA_Eta_R_L,ETA_Eta_F_L,ETA_EtaP_F_L,ETA_Eta_B,ETA_EtaP_B,ETA_Eta_B_RC,ETA_EtaP_B_RC,FMF,fragC,nHBAcc,nHBAcc2,nHBAcc3,nHBAcc_Lipinski,nHBDon,nHBDon_Lipinski,HybRatio,IC0,IC1,IC2,IC3,IC4,IC5,TIC0,TIC1,TIC2,TIC3,TIC4,TIC5,SIC0,SIC1,SIC2,SIC3,SIC4,SIC5,CIC0,CIC1,CIC2,CIC3,CIC4,CIC5,BIC0,BIC1,BIC2,BIC3,BIC4,BIC5,MIC0,MIC1,MIC2,MIC3,MIC4,MIC5,ZMIC0,ZMIC1,ZMIC2,ZMIC3,ZMIC4,ZMIC5,Kier1,Kier2,Kier3,nAtomLC,nAtomP,nAtomLAC,MLogP,McGowan_Volume,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,MLFER_A,MLFER_BH,MLFER_BO,MLFER_S,MLFER_E,MLFER_L,MPC2,MPC3,MPC4,MPC5,MPC6,MPC7,MPC8,MPC9,MPC10,TPC,piPC1,piPC2,piPC3,piPC4,piPC5,piPC6,piPC7,piPC8,piPC9,piPC10,TpiPC,R_TpiPCTPC,PetitjeanNu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+UMLYSI0118,L,methylcyclopentadiene dimer,CC1=CC2C3CC(C=C3C)C2C1,aromatic,C12H16,26472-00-4,n/a,UML,"2,5-Dimethyl-3a,4,7,7a-tetrahydro-1H-4,7-methanoindene",189.2,0,1.5312,2.34457344,51.5726,31.788688,0,0,28,12,16,0,12,0,0,0,0,0,0,0,0,0,1747.426476,2213.411102,3545.151814,2841.028359,1841.420961,1030.265549,399.614713,87.883488,9.144576,5579.542732,7765.022486,13008.14029,13005.18862,9687.437314,5689.039018,3122.337907,1154.675865,279.7536999,197.981616,219.449336,433.168024,578.480604,595.232964,402.962036,237.233188,143.482752,60.466176,40.58060648,56.86130896,98.31903254,107.969179,86.98579978,52.89440628,30.83975396,13.35045943,4.001516144,4480.214705,4225.081749,8862.493482,12357.74964,13859.49638,9703.319939,5593.174787,3692.500067,1664.258868,49.16666667,62.05555556,108.5555556,139.75,128.1111111,85,52,27,9,62.40808843,73.78037007,59.08586357,35.07442419,21.66377601,17.76319912,11.75337391,4.184928,1.016064,199.2693833,258.8340829,216.8023381,160.5578841,113.9698508,98.08687962,91.83346785,54.98456499,31.08374443,7.070772,7.314977867,7.219467067,7.141735852,7.002740753,6.94762131,6.977446706,6.832512,6.718464,1.449307374,1.895376965,1.638650542,1.332952827,1.02336235,0.911972522,0.907051587,0.635736163,0.444612905,160.0076681,140.8360583,147.7082247,152.5648103,163.0528986,167.2986196,164.5051408,175.8333365,184.917652,1.755952381,2.068518519,1.809259259,1.725308642,1.507189542,1.465517241,1.529411765,1.285714286,1,0.050905529,0.004217743,-0.00827758,-0.043317236,0.017157192,0.004599518,-0.013864359,0.007255213,0.006776745,830.1669189,79.06351608,158.1270322,-793.1058957,-133.4196834,182.8343809,-264.3686319,155.6562973,200.1295251,1543.730835,147.0219843,294.0439686,-1474.81428,-248.0995985,339.9883387,-491.60476,289.4495316,372.1493977,0.162624,0.015488,0.030976,-0.155364,-0.026136,0.035816,-0.051788,0.030492,0.039204,6.901195096,0.657256676,1.314513352,-6.593106029,-1.10912064,1.519906063,-2.19770201,1.293974081,1.663680961,37.48740129,3.570228694,7.140457389,-35.81385659,-6.024760922,8.256153856,-11.9379522,7.028887742,9.037141383,3.734126984,-0.512755102,0.615362812,-3.326105442,0.654336735,1.201530612,-1.105725624,-0.068452381,0.674744898,0.001818055,1.41E-04,-1.38E-04,-5.35E-04,2.02E-04,7.93E-05,-4.08E-04,3.45E-04,7.53E-04,29.64881853,2.635450536,2.635450536,-9.791430811,-1.569643334,3.152316913,-7.775547997,7.412204633,22.2366139,55.13324411,4.90073281,4.90073281,-18.2075837,-2.918818806,5.861867908,-14.45896353,13.78331103,41.34993308,0.005808,5.16E-04,5.16E-04,-0.001918074,-3.07E-04,6.18E-04,-0.001523176,0.001452,0.004356,0.246471253,0.021908556,0.021908556,-0.081396371,-0.013048478,0.026205277,-0.064638294,0.061617813,0.18485344,1.33883576,0.119007623,0.119007623,-0.442146378,-0.07087954,0.14234748,-0.351116241,0.33470894,1.00412682,0.133361678,-0.017091837,0.010256047,-0.04106303,0.007698079,0.020716045,-0.032521342,-0.003259637,0.074971655,0.07733069,-0.075883124,-0.294149962,0.111024893,0.043619174,-0.224292093,0.190030769,0.414163409,0.088888889,0.088888889,-0.330246914,-0.052941176,0.106321839,-0.262254902,0.25,0.75,0.088888889,0.088888889,-0.330246914,-0.052941176,0.106321839,-0.262254902,0.25,0.75,0.088888889,0.088888889,-0.330246914,-0.052941176,0.106321839,-0.262254902,0.25,0.75,0.088888889,0.088888889,-0.330246914,-0.052941176,0.106321839,-0.262254902,0.25,0.75,0.088888889,0.088888889,-0.330246914,-0.052941176,0.106321839,-0.262254902,0.25,0.75,-0.12816153,0.076904003,-0.30790727,0.057723323,0.155337315,-0.243858223,-0.024442083,0.562167906,1.000096072,1.120584679,1.344555968,0.840885103,0.870775307,1.208667786,0.418004626,2.10E-08,1.05,0.984375,1.336805556,0.972794118,0.780711207,1.158088235,0.5625,0,1.05,0.984375,1.336805556,0.972794118,0.780711207,1.158088235,0.5625,0,1.05,0.984375,1.336805556,0.972794118,0.780711207,1.158088235,0.5625,0,1.05,0.984375,1.336805556,0.972794118,0.780711207,1.158088235,0.5625,0,1.05,0.984375,1.336805556,0.972794118,0.780711207,1.158088235,0.5625,0,1.325611052,0.76825186,1.345200142,0.831755954,0.806863571,1.293711321,1.032943677,0,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,64.0130685,31.57235902,47.47158148,63.78220431,5.315183692,31.57235902,-0.346296296,0.04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+UMLYSI0127,L,"1,2,3,4,4a,5,6,7,8,8a-decahydronaphthalene",C1CCC2CCCCC2C1,aromatic,C10H18,91-17-8,7044,UML,"stereochemistry not specifiied (see below) + + + +cis: C1C[C@H]2CCCC[C@H]2CC1 + + + +trans: 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+UMLYSI0223,L,3-methylbutyl acetate,CC(C)CCOC(=O)C,saturated 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+UMLYSI0240,L,methyl (E)-2-methylpent-2-enoate,CC/C=C(\C)/C(=O)OC,unsaturated 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+UMLYSI0251,L,tert-butyl propanoate,CCC(=O)OC(C)(C)C,saturated 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+UMLYSI0258,L,methyl hex-5-enoate,COC(=O)CCCC=C,unsaturated 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+UMLYSI0266,L,3-methylbutyl formate,CC(C)CCOC=O,saturated 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+UMLYSI0269,L,methyl (E)-2-methylbut-2-enoate,C/C=C(\C)/C(=O)OC,unsaturated 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+UMLYSI0275,L,propyl 2-methylprop-2-enoate,CCCOC(=O)C(=C)C,unsaturated 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+UMLYSI0277,L,ethyl 3-methylbutanoate,CCOC(=O)CC(C)C,saturated 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+UMLYSI0279,L,methyl 3-methylbut-2-enoate,CC(=CC(=O)OC)C,unsaturated 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+UMLYSI0283,L,butyl acetate,CCCCOC(=O)C,saturated 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+UMLYSI0291,L,butan-2-yl acetate,CCC(C)OC(=O)C,saturated 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+UMLYSI0292,L,2-methylpropyl acetate,CC(C)COC(=O)C,saturated 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+UMLYSI0300,L,methyl (E)-pent-2-enoate,CC/C=C/C(=O)OC,unsaturated 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+UMLYSI0306,L,tert-butyl acetate,CC(=O)OC(C)(C)C,saturated 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+UMLYSI0307,L,methyl heptanoate,CCCCCCC(=O)OC,saturated 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+UMLYSI0315,L,"methyl 2,2-dimethylpropanoate",CC(C)(C)C(=O)OC,saturated 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+UMLYSI0323,L,tert-butyl formate,CC(C)(C)OC=O,saturated 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+UMLYSI0325,L,"2,2-dimethylbutanoic acid",CCC(C)(C)C(=O)O,saturated 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+UMLYSI0327,L,ethyl 2-methylbutanoate,CCC(C)C(=O)OCC,saturated 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+UMLYSI0329,L,methyl 2-methylprop-2-enoate,CC(=C)C(=O)OC,unsaturated 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+UMLYSI0334,L,methyl 3-methylbutanoate,CC(C)CC(=O)OC,saturated 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+UMLYSI0343,L,methyl hexanoate,CCCCCC(=O)OC,saturated 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+UMLYSI0347,L,ethyl 2-methylpropanoate,CCOC(=O)C(C)C,saturated 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+UMLYSI0355,L,ethyl 2-methylprop-2-enoate,CCOC(=O)C(=C)C,unsaturated 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+UMLYSI0358,L,methyl 2-methylbutanoate,CCC(C)C(=O)OC,saturated 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+UMLYSI0366,L,methyl pentanoate,CCCCC(=O)OC,saturated 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+UMLYSI0367,L,methyl 2-methylpropanoate,CC(C)C(=O)OC,saturated 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+UMLYSI0380,L,ethyl propanoate,CCC(=O)OCC,saturated 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+UMLYSI0382,L,methyl prop-2-enoate,COC(=O)C=C,unsaturated 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+UMLYSI0408,L,ethyl acetate,CCOC(=O)C,saturated 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+UMLYSI0415,L,dimethyl carbonate,COC(=O)OC,other multi-oxygen 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From adc43d33c4a3e7a1e33b434fb7c4e04e0604af5b Mon Sep 17 00:00:00 2001 From: Travis Kessler Date: Fri, 16 Aug 2019 12:24:24 -0400 Subject: [PATCH 05/40] Added alvaDesc-generated expanded YSI database --- databases/ysi_database_v2.0.csv | 568 ++++++++++++++++++++++++++++++++ 1 file changed, 568 insertions(+) create mode 100644 databases/ysi_database_v2.0.csv diff --git a/databases/ysi_database_v2.0.csv b/databases/ysi_database_v2.0.csv new file mode 100644 index 0000000..fe5a9ab --- /dev/null +++ b/databases/ysi_database_v2.0.csv @@ -0,0 +1,568 @@ 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+UMLYSI0118,L,methylcyclopentadiene dimer,CC1=CC2C3CC(C=C3C)C2C1,aromatic,C12H16,26472-00-4,n/a,UML,"2,5-Dimethyl-3a,4,7,7a-tetrahydro-1H-4,7-methanoindene",189.2,160.28,5.724285714,16.2144,27.0688,18.0912,31.3216,0.579085714,0.966742857,0.646114286,1.118628571,0.212121212,28,12,2,30,14,2,16,0,0,2,0,0,16,12,0,0,0,0,0,0,0,0,0,0,0,0,57.14285714,42.85714286,0,0,0,8,4,0,0,1,0,3,6,15,9,3,0.833333333,0.6,1.5,1,0.333333333,0,0,3,1,0,1,1,0,0,0,0,0,0,0,50.86746032,20.31468254,0,27.35595238,31.84166667,0,0,0,72,96,96,72,147.9,100.89537,90,115,115,90,181.42,122.127534,6,5.5,2.444444444,1.930555556,15,22,1.732050808,9.364262454,2,2.182247272,0.326785826,24,6,1.095212214,15,39.92749394,2.865679887,5.04396036,1,56,4.666666667,0.722222222,3.944444444,22,76,18,1.784159128,6,2.575757576,10,7.818181818,842,981,823,1094,11.48837398,125,2005,6.939271861,2.041538241,0.769135656,1.180565739,0.612202381,0.369022236,0.217957935,0.115229759,0.405138889,0.547453704,3.429166667,11.50008972,19.66666667,1.638888889,9.47471524,9.09023137,0.0547249,0.00140602,0.000971876,0.000348592,7.138888889,4.503609527,2.012098496,7.72E-06,0.005747693,0.002252721,0,5,0.729573959,2.708050201,4.290459441,5.198497031,6.142037406,7.072421901,8.017307508,8.954027759,9.897922094,10.83693034,11.78000934,3.36729583,0,4.762173935,3.433987204,6.343880434,5.860786223,8.026496939,8.002694162,9.771611931,2.708050201,3.135494216,3.496507561,3.891820298,4.043051268,4.094344562,4.043051268,3.850147602,3.258096538,1.609437912,2.833213344,3.36729583,3.784189634,4.276666119,4.532599493,4.744932128,4.890349128,4.912654886,4.418840608,2.708050201,12.27399761,5.926926026,6.606650186,1.114684097,0.67972416,3.256634863,2.607626977,8.29252874,5.754020195,5.776908128,5.034805569,4.686194486,3.564336147,0.691044062,0.411001443,0.262586733,0.157337674,0.097629052,0.06364886,7.878315178,5.110990332,4.875081908,4.131526387,3.646898872,2.612887827,0.656526265,0.365070738,0.221594632,0.1291102,0.07597706,0.046658711,8.29252874,5.754020195,5.776908128,5.034805569,4.686194486,3.564336147,34.52412117,2.436404985,81.10538611,5.110990332,5.754020195,4.037057091,4.875608856,134.6059378,27.58638781,0.985228136,2.254200348,5.874795113,3.022055209,3.559929884,148.7772229,998.7151692,1.459147917,3.502736172,12,1,43.01955001,2.306127543,1.462066091,14.7857466,0.40687982,0.560974582,1.363524187,0.985228136,2.448145765,3.726474118,4.039148672,4.324862958,4.324862958,27.58638781,68.54808141,104.3412753,113.0961628,121.0961628,121.0961628,0.204941834,0.509250056,0.775161014,0.840201886,0.899634628,0.899634628,3.822126786,2.359209158,1.080880804,0.76820625,0.482491964,0.482491964,0.197045627,0.489629153,0.745294824,0.807829734,0.864972592,0.864972592,20.13907068,7.904951107,0.658745926,1.077479026,2.563947676,0.213662306,4.81114526,15.80990221,1.317491851,6.135564891,3.497460064,2.918288281,0.24319069,0.558153829,0.127969956,0.010664163,-1.071470377,13.4750957,1.122924641,1.35543823,170,2.575757576,28.33333333,33.41666667,0.531458766,0.03796134,1.860105682,5.877735782,29.23358554,2.436132128,1.759058503,29.23358554,2.436132128,42.39094894,58.46717108,4.872264257,13.67796082,29.23358554,6.989335266,10.01917951,13.54313081,16.85764448,20.26024054,3.116114728,0.259676227,0.592336127,0.062323765,0.005193647,-1.446415583,13.59672208,1.133060174,1.360121065,107.49,14.39222083,1.203586906,4.605170186,28,2.333333333,1.736589638,5.083250939,0.423604245,4.806300989,17.61263494,1.467719578,12.64898446,6.07260843,4.615120517,6.023447593,7.502738211,9.016634256,10.54998852,3.464101615,0.288675135,0.647508748,3.464101615,0.288675135,0.647508748,13.45076492,1.120897077,1.354496379,0.959003366,35.06001956,13.96542847,0.997530605,48.87899964,1.629090003,3.383114068,0.281926172,0.635179969,1,0.083333333,1.874967772,6.766228136,0.563852345,2.044235393,2.746435327,1.773067336,0,1.454875625,0.220671362,1.257064891,3.151439665,0.262619972,0.598210796,0.33483605,0.027903004,-0.570201348,13.49392576,1.124493814,1.35616598,21.95694444,0.332680976,3.659490741,3.675308501,0.262522036,12.86357975,2.984698057,7.655441343,0.637953445,1.06076428,3.86189739,0.321824783,5.673078224,15.31088269,1.27590689,5.862169875,4.16043198,3.472221888,3.894460571,5.588328165,6.731762271,8.146588693,2.902137219,0.241844768,0.555261531,0.079081606,0.006590134,-1.322309422,13.47351607,1.122793005,1.355377133,516,7.818181818,86,10.16865079,0.165938137,0.011852724,0.58078348,7.76937861,86.3358262,7.194652183,2.323429259,86.3358262,7.194652183,104.5132983,172.6716524,14.38930437,25.89484865,86.3358262,9.03990786,13.35434298,17.83639789,22.29060219,26.74958554,2.523760672,0.210313389,0.482457802,0.061671074,0.005139256,-1.451902188,13.72000927,1.143334106,1.364825286,22.73174603,0.344420394,3.788624339,247.5,3.945809277,0.28184352,13.81033247,2.85098018,4.593348509,0.382779042,0.794555277,3.954016713,0.329501393,5.032354354,9.186697018,0.765558085,3.891150412,4.123346924,3.03280882,4.079657969,5.518234091,6.870789381,8.249661734,2.962305109,0.246858759,0.565955745,0.20934643,0.0174455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+UMLYSI0127,L,"1,2,3,4,4a,5,6,7,8,8a-decahydronaphthalene",C1CCC2CCCCC2C1,aromatic,C10H18,91-17-8,7044,UML,"stereochemistry not specifiied (see below) + + + +cis: C1C[C@H]2CCCC[C@H]2CC1 + + + +trans: 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alkanone,C9H18O,815-24-7,13152,PubChem,,65.8,142.27,5.081071429,14.456,27.2797,16.3071,31.9462,0.516285714,0.974275,0.582396429,1.140935714,0.2,28,10,7,27,9,1,10,2,0.074074074,1,0,0,18,9,0,1,0,0,0,0,0,0,0,0,1,0,64.28571429,32.14285714,0,3.571428571,0,8,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,48,90,101.7355372,50.1037111,126.06,101.5036163,51,80,83.63636364,53.99000833,124.8,92.5890368,7.833333333,6.916666667,2,1.666666667,7,15,3.872983346,3.871201011,1.276595745,1.472733358,0.508249709,21,5,1.234708048,12,30.82322605,2.670830083,7.654493014,1,35,3.5,0.6,3.52,15,72,10,1.295461844,4,2.466666667,4,2.466666667,405,564,273,484,9.555341511,57,111,9.675324675,2.373033188,2.884881456,2.295986656,0.65,0.295238095,0.085714286,0,1.446180556,4.486111111,8.972222222,33.01810606,23.16666667,2.316666667,7.184056211,5.47314386,0.028856616,0.006897229,-0.02221154,0.003392634,7.816666667,3.722515789,1.414509605,4.56969E-05,0.019518469,-0.045359878,9.06E-06,54,1.156779649,2.302585093,3.891820298,4.634728988,5.556828062,6.326149473,7.247792582,8.02059915,8.942591637,9.715892113,10.63799258,2.944438979,0,4.369447852,0,5.937536205,0,7.563719668,0,9.220191688,2.302585093,2.772588722,2.564949357,2.302585093,0,0,0,0,0,0,2.397895273,2.890371758,2.944438979,2.302585093,0,0,0,0,0,0,11.17532459,4.025351691,4.17438727,1.037024238,0.149035579,2.937832606,2.443680008,8.577350269,4.154700538,5.453738644,2.598076211,1.299038106,0,0.857735027,0.461633393,0.363582576,0.216506351,0.144337567,0,7.90824829,3.704124145,4.829124145,1.362372436,1.125,0,0.790824829,0.411569349,0.32194161,0.113531036,0.125,0,8.577350269,4.154700538,5.453738644,2.598076211,1.299038106,0,25.5055535,1.775162974,45.98505863,3.690221478,4.095627583,2.906141204,3.224729682,134.6059378,31.01796278,1.107784385,1.96559623,5.361455055,1.570950594,3.298030961,88.45183037,595.1215111,1.156779649,3.016875696,4,0.4,15.70950594,1.958499469,1.402025991,26.55266679,1.039577419,1.386472124,3.681549487,1.107784385,1.501339742,1.501339742,1.501339742,1.329648495,0.886003325,31.01796278,42.03751279,42.03751279,42.03751279,37.23015787,24.8080931,0.230435323,0.312300582,0.312300582,0.312300582,0.276586297,0.184301625,3.699570537,3.30601518,3.30601518,3.30601518,3.477706427,3.921351597,0.230435323,0.312300582,0.312300582,0.312300582,0.276586297,0.184301625,37.39230485,4.80842881,0.480842881,0.682003191,2.334414218,0.233441422,4.668828437,9.61685762,0.961685762,3.713572067,3.170763419,2.891951981,0.289195198,0.461191077,0.557537762,0.055753776,-0.253725712,8.475616112,0.847561611,0.928171278,111,2.466666667,22.2,22.75,0.470125798,0.0522362,4.231132186,5.379897354,22.84770066,2.284770066,1.3588425,22.84770066,2.284770066,32.43027636,45.69540133,4.569540133,12.76546841,22.84770066,6.466144724,9.303921786,12.54649066,15.63111366,18.77854053,2.815656588,0.281565659,0.449579685,0,0,0,8.697235167,0.869723517,0.939381213,111,-10.1581629,1.916515139,0,18,1.8,1.255272505,5.528917957,0.552891796,5.320205805,15.50551323,1.550551323,8.432288684,6.004665238,4.204692619,5.726847748,7.333676396,8.971956271,10.62582939,3.16227766,0.316227766,0.5,3.16227766,0.316227766,0.5,8.581163303,0.85811633,0.933546167,0.830940108,20.66025404,8.960938896,0.995659877,80.64845006,1.405447334,2.366025404,0.23660254,0.374019403,1,0.1,2,4.732050808,0.473205081,1.681758574,2.575621127,1.609437912,0,1.446918983,0,1.354545663,3.000973155,0.300097315,0.47726211,0.643950551,0.064395055,-0.191147481,8.47871515,0.847871515,0.928330045,14.64583333,0.325462963,2.929166667,2.606174118,0.289574902,23.45556706,2.594009071,5.257194925,0.525719492,0.72075408,3.280925887,0.328092589,5.129189492,10.51438985,1.051438985,4.311832836,3.63502264,3.055961937,3.394010157,4.950141615,5.888801823,7.184028701,2.794741249,0.279474125,0.446341605,0.374073442,0.037407344,-0.427043124,8.482703211,0.848270321,0.928534272,111,2.466666667,22.2,22.75,0.470125798,0.0522362,4.231132186,5.379897354,22.84770066,2.284770066,1.3588425,22.84770066,2.284770066,32.43027636,45.69540133,4.569540133,12.76546841,22.84770066,6.466144724,9.303921786,12.54649066,15.63111366,18.77854053,2.815656588,0.281565659,0.449579685,0,0,0,8.697235167,0.869723517,0.939381213,45,1,9,45,1,0.111111111,9,3.828641396,9,0.9,0.954242509,9,0.9,10,18,1.8,8.318498321,9.000531868,4.510859507,6.580639137,8.790421307,10.9859874,13.18336628,0,0,0,0,0,0,0,0,0,105.625,1.920454545,21.1,25.64752791,0.49350496,0.054833884,4.441544644,5.306913129,43.38258141,21.8162907,2.18162907,1.338780912,21.8162907,2.18162907,31.3988664,43.58258141,4.358258141,12.15267162,21.81629071,0.223143551,6.384509088,9.154096935,12.36766863,15.396746,18.50310326,2.815656588,0.281565659,0.449579685,0,0,0,8.697235167,0.869723517,0.939381213,105.6271,1.920492727,21.10048,25.64415992,0.493493409,0.054832601,4.441440682,5.306943433,43.38401534,21.81670767,2.181670767,1.338789212,21.81670767,2.181670767,31.39928336,43.58353534,4.358353534,12.15327559,21.81670767,0.222663436,6.384543769,9.154159109,12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alkanone,C9H18O,821-55-6,13187,PubChem,,45.1,142.27,5.081071429,14.456,27.2797,16.3071,31.9462,0.516285714,0.974275,0.582396429,1.140935714,0.2,28,10,3,27,9,1,10,6,0.222222222,1,0,0,18,9,0,1,0,0,0,0,0,0,0,0,1,0,64.28571429,32.14285714,0,3.571428571,0,8,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,36,78,89.73553719,38.1037111,103.46,85.509344,34,58,61.63636364,35.32889259,82.76,62.91136,6.333333333,5.416666667,2.583333333,2.166666667,1,9,3,5.257495372,1.578947368,1.691726385,0.436124566,21,1,1.421594772,7,34.31757639,4.07703596,5.939658627,1,64,6.4,1.2,5.92,24,76,20,2.246439345,8,3.511111111,8,3.511111111,576,820,461,852,10.88209949,111,158,9.675324675,6.790594136,8.825130123,6.570120301,0.501666667,0.219209957,0.093805067,0.046160268,1.336575491,3.476438571,7.35163407,22.33791838,21.66666667,2.166666667,7.46575419,5.383631714,0.042459057,0.006130567,-0.022182193,0.003418219,7.666666667,3.722939441,1.309435081,4.71537E-05,0.019457175,-0.046452233,6.85E-06,22,2.246439345,2.302585093,3.610917913,4.234106505,4.919980926,5.564520407,6.255750042,6.908754779,7.601402335,8.258163362,8.951180966,2.944438979,0,4.007333185,0,5.198497031,0,6.447305863,0,7.727094485,2.302585093,2.302585093,2.079441542,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0,0,2.397895273,2.48490665,2.197224577,2.079441542,1.945910149,1.791759469,1.609437912,1.386294361,0,0,9.655923227,4.025351691,4.17438727,1.037024238,0.149035579,2.97872113,2.426530506,7.819990956,4.77005561,3.596735547,1.885027805,1.156139248,0.692513903,0.781999096,0.530006179,0.399637283,0.269289686,0.192689875,0.138502781,7.150888978,4.264784317,2.866228665,1.632392159,0.97749887,0.566196079,0.715088898,0.473864924,0.318469852,0.23319888,0.162916478,0.113239216,7.819990956,4.77005561,3.596735547,1.885027805,1.156139248,0.692513903,29.19768393,2.219823261,36.85517875,4.250881649,4.690097211,3.518417025,4.056418566,134.6059378,31.01796278,1.107784385,2.856643375,5.238426519,2.521928095,3.290839978,128.5489519,827.6713899,1.295461844,3.239097918,10,1,33.21928095,2.771732595,1.37310439,27.5152295,0.586040345,0.940536678,1.48881419,1.107784385,1.712058833,3.040071227,3.69951385,4.039148672,4.039148672,31.01796278,47.93764732,85.12199436,103.5863878,113.0961628,113.0961628,0.230435323,0.356133229,0.632379193,0.769552885,0.840201886,0.840201886,3.699570537,3.095296089,1.767283695,1.107841072,0.76820625,0.76820625,0.230435323,0.356133229,0.632379193,0.769552885,0.840201886,0.840201886,42.93050049,5.67128182,0.567128182,0.753681229,1.969615506,0.196961551,3.939231012,11.34256364,1.134256364,4.343805422,3.114238058,2.929791425,0.292979142,0.466836704,0.256323536,0.025632354,-0.591211514,8.553825136,0.855382514,0.932160368,158,3.511111111,31.6,19.67857143,0.308132095,0.034236899,2.773188856,6.118097198,32.77768137,3.277768137,1.515578229,32.77768137,3.277768137,52.23678852,65.55536274,6.555536274,13.2666611,32.77768137,7.311218384,10.22734484,14.07689049,17.37196889,20.98136027,2.878126441,0.287812644,0.459109869,0.062731007,0.006273101,-1.202517742,8.650312188,0.865031219,0.937031781,158,-14.45936702,3.888024175,0,18,1.8,1.255272505,4.235660391,0.423566039,4.132780372,13.0706908,1.30706908,8.762333041,5.109111885,4.007333185,5.231108617,6.52649486,7.85902698,9.214232787,3.16227766,0.316227766,0.5,3.16227766,0.316227766,0.5,8.581163303,0.85811633,0.933546167,0.954011122,17.32780492,8.986598879,0.998510987,80.87938991,1.542591551,3.078049832,0.307804983,0.488275647,1,0.1,2,6.156099665,0.615609966,2.046347853,2.621963198,1.819158443,0,1.644912301,0,1.542922129,3.115355072,0.311535507,0.493507552,0.298858491,0.029885849,-0.524534401,8.554710571,0.855471057,0.932205321,12.80636338,0.284585853,2.561272676,3.373575501,0.374841722,30.36217951,2.505180768,5.867786968,0.586778697,0.768474338,2.707556429,0.270755643,4.341933877,11.73557394,1.173557394,4.715434921,3.371717481,3.014081044,2.887100403,4.420977319,4.990567602,6.158555507,2.876309257,0.287630926,0.458835579,0.183857134,0.018385713,-0.735519514,8.556822595,0.855682259,0.932312528,158,3.511111111,31.6,19.67857143,0.308132095,0.034236899,2.773188856,6.118097198,32.77768137,3.277768137,1.515578229,32.77768137,3.277768137,52.23678852,65.55536274,6.555536274,13.2666611,32.77768137,7.311218384,10.22734484,14.07689049,17.37196889,20.98136027,2.878126441,0.287812644,0.459109869,0.062731007,0.006273101,-1.202517742,8.650312188,0.865031219,0.937031781,45,1,9,45,1,0.111111111,9,3.828641396,9,0.9,0.954242509,9,0.9,10,18,1.8,8.318498321,9.000531868,4.510859507,6.580639137,8.790421307,10.9859874,13.18336628,0,0,0,0,0,0,0,0,0,152.625,2.775,30.5,22.25634924,0.317189256,0.035243251,2.854703304,6.062276076,62.93280857,31.59140428,3.159140428,1.499568931,31.59140428,3.159140428,50.87529316,63.13280857,6.313280857,12.66599324,31.59140428,0.223143551,7.250702046,10.09341665,13.9423487,17.17570037,20.76777347,2.876715673,0.287671567,0.458896939,0.105803197,0.01058032,-0.97550121,8.648093523,0.864809352,0.93692037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+UMLYSI0223,L,3-methylbutyl acetate,CC(C)CCOC(=O)C,saturated 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+UMLYSI0255,L,ethyl 3-methylbut-2-enoate,CCOC(=O)C=C(C)C,unsaturated 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+UMLYSI0260,L,methyl (E)-pent-3-enoate,C/C=C/CC(=O)OC,unsaturated 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+UMLYSI0275,L,propyl 2-methylprop-2-enoate,CCCOC(=O)C(=C)C,unsaturated 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+UMLYSI0283,L,butyl acetate,CCCCOC(=O)C,saturated 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alkanes,C7H16,142-82-5,8900,PubChem,,36,100.23,4.357826087,11.2144,22.0688,13.0912,26.3216,0.487582609,0.959513043,0.569182609,1.144417391,0.285714286,23,7,2,22,6,0,6,4,0.181818182,0,0,0,16,7,0,0,0,0,0,0,0,0,0,0,0,0,69.56521739,30.43478261,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22,22,22,22,30.74,23.5256,20,20,20,20,28.08,21.4128,4.5,4.5,2,2,0,5,2.236067977,3.465735903,1.555555556,1.640670712,0.537158271,14,0,1.707106781,4,19.24902768,3.055050463,3.627465013,1,33,4.714285714,0.897959184,2.857142857,12,28,9,1.950212065,6,2.666666667,6,2.666666667,202,202,146,146,7.297142506,54,56,7,6,6,6,0.452380952,0.219387755,0.096598639,0.042857143,0.138888889,0.245694444,0.982777778,7.634054137,11.5,1.642857143,5.496696467,3.821367205,0.166737152,-0.026921964,0.10782785,0.031746032,7.142857143,2.621867655,0.853070277,0.001031181,-0.107445869,0.247945507,0.000358258,13,1.950212065,1.945910149,3.135494216,3.713572067,4.317488114,4.919980926,5.533389489,6.142037406,6.758094504,7.368339686,7.985143931,2.564949357,0,3.496507561,0,4.574710979,0,5.720311777,0,6.900730664,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,8.761236807,3.36729583,3.36729583,1,0,2.627114062,2.132338507,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,0.790790558,0.569035594,0.412132034,0.301776695,0.225592232,0.176776695,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,0.790790558,0.569035594,0.412132034,0.301776695,0.225592232,0.176776695,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,20.48528137,1.840004522,19.78207923,3.414213562,3.414213562,2.888448395,3.250213275,104.041925,20.39044053,0.886540893,2.398302995,4.168986958,1.950212065,2.774244972,50.3643629,233.4632697,0.863120569,2.751629167,4,0.571428571,13.65148445,2.440007983,1.22222776,15.01828953,0.666081376,1.019498601,1.729713704,0.886540893,1.149229762,2.024260067,2.566190217,2.805745652,2.609069045,20.39044053,26.43228451,46.55798153,59.02237498,64.53214998,60.00858803,0.195982923,0.254054166,0.447492504,0.567294146,0.620251403,0.576773143,3.637021063,3.374332195,2.499301889,1.95737174,1.717816305,1.914492911,0.19880132,0.257707677,0.453927819,0.575452308,0.629171135,0.58506762,20.48528137,4.027339492,0.575334213,0.423512769,1.847759065,0.263965581,3.69551813,8.054678984,1.150668426,3.091042453,2.73919265,2.513669746,0.359095678,0.280215753,0.099456184,0.014208026,-0.701657747,5.592530245,0.798932892,0.523325839,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,56,-7.498040439,2.885095623,0,12,1.714285714,0.755426872,3.801937736,0.543133962,3.603875472,8.702204129,1.243172018,5.934894196,4.522970604,3.555348061,4.691347882,5.894402834,7.133295955,8.394121194,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.975489589,10.82842712,5.985143843,0.997523974,35.91086306,1.310251729,2.366025404,0.338003629,0.261813582,1,0.142857143,2,4.732050808,0.676007258,1.681758574,2.316436311,1.609437912,0,1.446918983,0,1.354545663,2.618591722,0.374084532,0.292647453,0.169101979,0.024157426,-0.540295917,5.594918316,0.799274045,0.523455625,7.989722222,0.380462963,2.282777778,2.564019422,0.42733657,15.38411653,2.102137974,4.119483043,0.588497578,0.430389904,2.400399559,0.342914223,3.963351092,8.238966087,1.176995155,3.346584212,2.935808684,2.621413019,2.328341703,3.846629402,4.257734494,5.383121673,2.490590677,0.355798668,0.277411651,0.067967869,0.009709696,-0.817387444,5.593895954,0.799127993,0.523400069,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,56,2,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,33.2507646,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,0,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,56,2,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,33.2507646,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,0,5.973809612,8.168769824,11.3726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alkanone,C7H14O,12/6/7379,23847,PubChem,,35.9,114.21,5.191363636,11.4024,21.5125,12.7843,25.1158,0.518290909,0.977840909,0.581104545,1.141627273,0.25,22,8,4,21,7,1,8,3,0.142857143,1,0,0,14,7,0,1,0,0,0,0,0,0,0,0,1,0,63.63636364,31.81818182,0,4.545454545,0,6,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,30,72,83.73553719,32.1037111,96.8,79.500112,30,56,59.63636364,31.99333888,82.36,60.785424,5.666666667,4.75,2.027777778,1.666666667,2,8,2.828427125,3.583518938,1.411764706,1.56508458,0.528256863,17,2,1.37492571,7,22.7132832,2.777460299,5.19712921,0.666666667,33,4.125,0.65625,3.125,12,44,12,1.561278124,5,2.5,5,2.5,254,382,175,342,7.977658423,54,70,7.675324675,3.649726375,3.379468568,3.501604363,0.541666667,0.287690781,0.09243112,0.031702899,1.284722222,3.742824074,7.541759259,16.34249339,19,2.375,5.772900051,4.064807754,0.058043817,0.022024823,-0.071642195,0.010203255,7.875,2.968550645,1.045989943,0.000328773,0.062656093,-0.143631753,0.000101807,24,1.5,2.079441542,3.433987204,4.110873864,4.844187086,5.556828062,6.289715571,7.01301579,7.744569809,8.471568013,9.202207389,2.708050201,0,3.850147602,0,5.176149733,0,6.577861358,0,8.011355109,2.079441542,2.197224577,2.079441542,1.609437912,1.098612289,0,0,0,0,0,2.197224577,2.397895273,2.397895273,1.791759469,1.098612289,0,0,0,0,0,9.859535665,3.610917913,3.784189634,1.047985505,0.173271721,2.740584594,2.250498705,6.568914101,3.680739213,3.009975955,1.882088123,0.788675135,0.333333333,0.821114263,0.525819888,0.376246994,0.268869732,0.197168784,0.166666667,5.899812122,3.20815999,2.371013381,1.140350191,0.535074775,0.288675135,0.737476515,0.45830857,0.296376673,0.16290717,0.133768694,0.144337567,6.568914101,3.680739213,3.009975955,1.882088123,0.788675135,0.333333333,22.66178554,1.788495438,27.7911812,3.194257322,3.613264398,2.618392545,2.97130605,98.10749561,25.15304225,1.143320102,2.189391747,4.636375967,2.25,2.967432933,61.30296891,324.5463177,1.5,2.842370993,7,0.875,22,2.222274414,1.299903619,18.39747893,0.849205499,1.189826589,2.609981172,1.143320102,1.820438145,2.889916845,3.265583323,3.265583323,2.860180448,25.15304225,40.0496392,63.5781706,71.8428331,71.8428331,62.92396987,0.256382472,0.408222011,0.648046005,0.732286893,0.732286893,0.641377802,3.316111517,2.638993473,1.569514773,1.193848296,1.193848296,1.59925117,0.256382472,0.408222011,0.648046005,0.732286893,0.732286893,0.641377802,25.76517449,4.323526474,0.540440809,0.508670498,2.074313293,0.259289162,4.148626586,8.647052947,1.080881618,3.33220451,2.900309802,2.591794958,0.32397437,0.330880513,0.01080009,0.001350011,-1.573258115,6.543951602,0.81799395,0.652672063,70,2.5,17.5,14.73333333,0.452974194,0.064710599,3.170819357,4.9698133,18.18147764,2.272684705,1.007703341,18.18147764,2.272684705,27.13286733,36.36295528,4.54536941,10.21880911,18.18147768,6.070737728,8.562740006,11.66038812,14.47232617,17.41662318,2.509642727,0.313705341,0.31968952,0.105383517,0.01317294,-0.78178185,6.710512208,0.838814026,0.661404537,70,-8.118872577,2.22928515,0,14,1.75,0.916902429,4.641186476,0.58014831,4.416899332,11.05828432,1.38228554,6.767343125,5.082415225,3.80666249,5.105945474,6.49375384,7.928406026,9.392411898,2.828427125,0.353553391,0.361235995,2.828427125,0.353553391,0.361235995,6.599663291,0.824957911,0.655617423,0.920184803,14.05985573,6.968636025,0.995519432,48.78045218,1.34917864,2.403775915,0.300471989,0.304715183,1,0.125,2,4.80755183,0.600943979,1.704748092,2.412878418,1.620487749,0,1.440727013,0,1.33525598,2.750794013,0.343849252,0.351566456,0,0,0,6.548508152,0.818563519,0.652913898,10.10777778,0.360992063,2.526944444,2.558547764,0.365506823,17.90983435,2.288132279,4.527647899,0.565955987,0.524698117,2.764294688,0.345536836,4.46836837,9.055295797,1.131911975,3.632325892,3.19574249,2.785658533,2.782160718,4.309309027,4.987884204,6.177956811,2.532022741,0.316502843,0.322774082,0.009052933,0.001131617,-1.634568565,6.547999583,0.818499948,0.652886914,70,2.5,17.5,14.73333333,0.452974194,0.064710599,3.170819357,4.9698133,18.18147764,2.272684705,1.007703341,18.18147764,2.272684705,27.13286733,36.36295528,4.54536941,10.21880911,18.18147768,6.070737728,8.562740006,11.66038812,14.47232617,17.41662318,2.509642727,0.313705341,0.31968952,0.105383517,0.01317294,-0.78178185,6.710512208,0.838814026,0.661404537,28,1,7,28,1,0.142857143,7,3.36729583,7,0.875,0.676078432,7,0.875,8,14,1.75,6.647688374,7.003254818,4.043051268,5.82008293,7.786967003,9.729193687,11.67552889,0,0,0,0,0,0,0,0,0,65.875,1.829861111,16.4375,17.41399965,0.481204286,0.068743469,3.368429999,4.883452752,34.10792431,17.17896215,2.147370269,0.987997538,17.17896215,2.147370269,26.13095327,34.29542431,4.286928038,9.5948276,17.17896227,0.223143551,5.97516048,8.367896208,11.44739496,14.1789405,17.08199232,2.507670756,0.313458844,0.319416412,0.120565753,0.015070719,-0.735020829,6.709674881,0.83870936,0.661361181,65.8765,1.829902778,16.43795,17.41076486,0.481190573,0.06874151,3.368334013,4.883488244,34.10932449,17.17936224,2.14742028,0.98800563,17.1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compounds,C7H14O3,623-96-1,69344,PubChem,,34.5,146.21,6.092083333,12.832,24.1671,13.6933,27.5346,0.534666667,1.0069625,0.570554167,1.147275,0.2,24,10,3,23,9,1,10,6,0.260869565,1,0,0,14,7,0,3,0,0,0,0,0,0,0,0,3,0,58.33333333,29.16666667,0,12.5,0,6,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,36,142,177.2066116,45.85189625,190.86,152.697056,35,108,122.5454545,42.64113239,169.04,117.44096,6.333333333,4.75,2.666666667,1.791666667,1,9,3,5.257495372,1.578947368,1.691726385,0.436124566,23,1,1.201312089,8,33.69463969,3.797660098,5.83121472,1,61,6.1,1.12,5.76,21,88,20,2.246439345,8,3.311111111,8,3.311111111,541,964,425,1124,10.69962648,105,149,9.597402597,6.713716797,6.597402597,6.443424302,0.493333333,0.24982906,0.11663438,0.066096256,2.218796296,3.516388889,11.56111111,16.55246251,25.66666667,2.566666667,6.901805996,4.481754126,0.018417917,0.007783947,-0.026104624,0.001252288,8.066666667,3.675880803,1.26968644,3.95692E-05,0.019799689,-0.04743374,5.33E-06,22,2.246439345,2.302585093,3.610917913,4.262679877,4.96284463,5.645446898,6.354370041,7.048386409,7.760040681,8.458504195,9.171079658,2.944438979,0,4.007333185,0,5.231108617,0,6.532334292,0,7.878155337,2.302585093,2.302585093,2.197224577,2.079441542,1.945910149,1.386294361,1.098612289,0.693147181,0,0,2.397895273,2.48490665,2.397895273,2.302585093,2.197224577,1.386294361,1.098612289,0.693147181,0,0,9.849348106,4.025351691,4.17438727,1.037024238,0.149035579,2.978202856,2.429006717,7.819990956,4.808060412,3.389628766,2.100953631,1.249509283,0.90104757,0.781999096,0.534228935,0.376625418,0.262619204,0.178501326,0.150174595,6.553171996,3.603936267,1.946923425,0.848074696,0.412457479,0.225592232,0.6553172,0.400437363,0.216324825,0.106009337,0.058922497,0.037598705,7.819990956,4.808060412,3.389628766,2.100953631,1.249509283,0.90104757,31.24220933,2.182440934,37.76575545,3.522905582,4.473904056,2.944598175,3.395664559,110.0391,32.32975915,1.347073298,2.75907983,5.262596461,2.521928095,3.285553869,124.1585923,784.1268728,1.295461844,3.239097918,6,0.6,25.21928095,2.71928147,1.380538476,27.23909308,0.651882599,1.007443326,1.751451784,1.347073298,2.03067213,2.938721876,2.938721876,2.938721876,2.938721876,32.32975915,48.73613111,70.52932501,70.52932501,70.52932501,70.52932501,0.293802468,0.442898307,0.640947854,0.640947854,0.640947854,0.640947854,3.237889203,2.554290371,1.646240625,1.646240625,1.646240625,1.646240625,0.293802468,0.442898307,0.640947854,0.640947854,0.640947854,0.640947854,43.27254371,5.959003237,0.595900324,0.775173621,2.028523488,0.202852349,4.057046976,11.91800647,1.191800647,4.394449155,3.114674477,2.847079481,0.284707948,0.454399591,0.153454161,0.015345416,-0.814021332,8.546548771,0.854654877,0.931790775,149,3.311111111,29.8,20.02738095,0.333157677,0.03701752,2.998419094,5.991464547,31.09689182,3.109689182,1.492716983,31.09689182,3.109689182,47.67861055,62.19378363,6.219378363,13.2497693,31.09689182,7.169350017,10.13463896,13.8281059,17.14101845,20.64546363,2.673467888,0.267346789,0.427074972,0,0,0,8.673332129,0.867333213,0.938185977,149,-13.63573219,3.316343748,0,18,1.8,1.255272505,4.377202854,0.437720285,4.256588096,13.0609573,1.30609573,8.752265314,5.124278453,4.007333185,5.231108617,6.532334292,7.878155337,9.253495642,3.16227766,0.316227766,0.5,3.16227766,0.316227766,0.5,8.581163303,0.85811633,0.933546167,0.961612082,17.45945742,8.976015514,0.997335057,80.78413962,1.555480766,3.34334402,0.334334402,0.524181067,1,0.1,2,6.686688041,0.668668804,2.11147503,2.628125014,1.84582669,0,1.652923024,0,1.542427202,3.115355072,0.311535507,0.493507552,0.022716116,0.002271612,-1.643665926,8.552012809,0.855201281,0.932068343,12.95616355,0.287914746,2.59123271,3.357786759,0.373087418,30.22008083,2.511944667,5.81397518,0.581397518,0.764473173,2.79540204,0.279540204,4.466562422,11.62795036,1.162795036,4.696674299,3.390199122,3.015691861,2.913559541,4.462165709,5.097299558,6.286414413,2.78979442,0.278979442,0.445572201,0.112063525,0.011206352,-0.950535722,8.549836308,0.854983631,0.9319578,149,3.311111111,29.8,20.02738095,0.333157677,0.03701752,2.998419094,5.991464547,31.09689182,3.109689182,1.492716983,31.09689182,3.109689182,47.67861055,62.19378363,6.219378363,13.2497693,31.09689182,7.169350017,10.13463896,13.8281059,17.14101845,20.64546363,2.673467888,0.267346789,0.427074972,0,0,0,8.673332129,0.867333213,0.938185977,45,1,9,45,1,0.111111111,9,3.828641396,9,0.9,0.954242509,9,0.9,10,18,1.8,8.318498321,9.000531868,4.510859507,6.580639137,8.790421307,10.9859874,13.18336628,0,0,0,0,0,0,0,0,0,121.625,2.211363636,24.25,26.93216964,0.414263531,0.046029281,3.728371777,5.641380558,50.54995101,25.64997551,2.564997551,1.409086955,25.64997551,2.564997551,39.07985279,51.14995101,5.114995101,11.58901864,25.64997551,0.559615788,6.781022154,9.563528423,13.05333163,16.18187574,19.48779924,2.631759437,0.263175944,0.420246189,0,0,0,8.682792891,0.868279289,0.938659442,121.6799,2.212361818,24.26116,26.91612303,0.414056405,0.046006267,3.726507641,5.642179109,50.57383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+UMLYSI0303,L,3-methoxy-3-methylbutan-1-ol,CC(C)(CCO)OC,other multi-oxygen compounds,C6H14O2,56539-66-3,62118,PubChem,,33.2,118.2,5.372727273,11.1172,21.8398,12.2388,25.3252,0.505327273,0.992718182,0.556309091,1.151145455,0.25,22,8,4,21,7,0,7,3,0.142857143,0,0,0,14,6,0,2,0,0,0,0,0,0,0,0,2,0,63.63636364,27.27272727,0,9.090909091,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,32,88,107.8852158,36.98224072,114.82,94.74760144,32,60,66.06060606,35.65445462,90.96,68.645104,5.75,4.616666667,2,1.183333333,3,9,3,3.465735903,1.391304348,1.542210825,0.537158271,18,2,1.19235154,7,22.35055152,2.638993315,5.086326547,0.666666667,32,4,0.5,2.8125,11,46,12,1.5,5,2.392857143,5,2.392857143,244,424,163,475,7.847063842,52,67,7.922077922,3.049668929,3.602663936,3.019964362,0.554166667,0.282967836,0.091282895,0.015625,1.407986111,2.452986111,7.942083333,14.27203725,19.75,2.46875,5.591511471,3.851263724,0.044977544,0.024063093,-0.076135762,0.007652985,7.96875,2.936296453,1.020094495,0.000301839,0.063323957,-0.145566546,8.99498E-05,24,1.5,2.079441542,3.496507561,4.17438727,4.990432587,5.693732139,6.517671273,7.22620901,8.0522965,8.762019954,9.588708312,2.708050201,0,3.931825633,0,5.361292166,0,6.86171134,0,8.385944905,2.079441542,2.302585093,2.079441542,1.609437912,0.693147181,0,0,0,0,0,2.079441542,2.302585093,2.079441542,1.609437912,0.693147181,0,0,0,0,0,10.19346698,3.610917913,3.610917913,1,0,2.7363824,2.254805845,6.621320344,3.621320344,3.267766953,1.883883476,0.853553391,0.176776695,0.827665043,0.517331478,0.363085217,0.269126211,0.213388348,0.176776695,5.769675448,2.782153592,2.437423582,1.266451329,0.371312417,0.045643546,0.721209431,0.397450513,0.270824842,0.180921618,0.092828104,0.045643546,6.621320344,3.621320344,3.267766953,1.883883476,0.853553391,0.176776695,23.49568901,1.736543755,28.744119,2.718907669,3.370802501,2.163083419,2.497846022,98.10749561,27.2947517,1.240670532,2.099058711,4.649763026,2.405639062,2.96531293,58.7736439,311.5341228,1.405639062,2.807354922,7,0.875,22,2.116729297,1.304121287,18.29373854,0.931000364,1.25913758,3.070635056,1.240670532,2.181899058,3.265583323,3.265583323,3.265583323,2.860180448,27.2947517,48.00177927,71.8428331,71.8428331,71.8428331,62.92396987,0.278212705,0.489277389,0.732286893,0.732286893,0.732286893,0.641377802,3.218761087,2.277532561,1.193848296,1.193848296,1.193848296,1.59925117,0.282463768,0.496753501,0.743476167,0.743476167,0.743476167,0.651177994,25.3492424,4.259629201,0.53245365,0.503497436,2.156639379,0.269579922,4.313278757,8.519258402,1.0649073,3.258096538,2.912496854,2.552449407,0.319056176,0.325565714,0.436096047,0.054512006,-0.28833428,6.529170654,0.816146332,0.651886416,67,2.392857143,16.75,15.03333333,0.48191165,0.068844521,3.373381553,4.882801923,17.44264589,2.180330737,0.993289891,17.44264589,2.180330737,25.36163497,34.88529179,4.360661473,10.15615107,17.44264597,5.96870756,8.464003363,11.47977191,14.27456762,17.1623407,2.53736567,0.317170709,0.323506448,0.116490414,0.014561302,-0.746967849,6.717902604,0.839737825,0.661786963,67,-7.770920895,1.970157536,0,14,1.75,0.916902429,5.173197841,0.64664973,4.919411029,10.96036971,1.370046214,6.72982407,5.40716846,3.850147602,5.241747015,6.751101469,8.319961022,9.92088533,2.828427125,0.353553391,0.361235995,2.828427125,0.353553391,0.361235995,6.599663291,0.824957911,0.655617423,0.905330086,14.48528137,6.958634325,0.994090618,48.71044028,1.337802448,2.3911917,0.298898963,0.302891516,1,0.125,2,4.7823834,0.597797925,1.693319396,2.410003684,1.609437912,0,1.41706602,0,1.304948722,2.737460871,0.342182609,0.349878336,0.377964473,0.047245559,-0.338039216,6.536487976,0.817060997,0.652275573,10.31777778,0.368492063,2.579444444,2.476066668,0.35372381,17.33246667,2.301780896,4.475992721,0.55949909,0.520711497,2.859310651,0.357413831,4.610785777,8.951985441,1.11899818,3.56490681,3.24110399,2.7931441,2.880323208,4.408983917,5.158116151,6.368310339,2.473163292,0.309145412,0.314602233,0.305027059,0.038128382,-0.412529306,6.534919462,0.816864933,0.652192192,67,2.392857143,16.75,15.03333333,0.48191165,0.068844521,3.373381553,4.882801923,17.44264589,2.180330737,0.993289891,17.44264589,2.180330737,25.36163497,34.88529179,4.360661473,10.15615107,17.44264597,5.96870756,8.464003363,11.47977191,14.27456762,17.1623407,2.53736567,0.317170709,0.323506448,0.116490414,0.014561302,-0.746967849,6.717902604,0.839737825,0.661786963,28,1,7,28,1,0.142857143,7,3.36729583,7,0.875,0.676078432,7,0.875,8,14,1.75,6.647688374,7.003254818,4.043051268,5.82008293,7.786967003,9.729193687,11.67552889,0,0,0,0,0,0,0,0,0,61,1.694444444,15.1875,17.38637637,0.528997248,0.075571035,3.702980739,4.70896698,31.00908221,15.7545411,1.969317638,0.957924606,15.7545411,1.969317638,23.05410272,31.38408221,3.923010276,9.368913402,15.75454158,0.405465108,5.764014863,8.155676186,11.07520036,13.76375569,16.55267028,2.601446676,0.325180835,0.332171943,0.155170227,0.019396278,-0.647353284,6.702375829,0.837796979,0.660983021,61.0144,1.694844444,15.19125,17.37954747,0.528872116,0.075553159,3.702104811,4.709391528,31.01827876,15.758539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alkanone,C6H12O,108-10-1,7909,PubChem,,32.8,100.18,5.272631579,9.8756,18.6289,11.0229,21.7006,0.519768421,0.980468421,0.580152632,1.142136842,0.285714286,19,7,4,18,6,1,7,2,0.111111111,1,0,0,12,6,0,1,0,0,0,0,0,0,0,0,1,0,63.15789474,31.57894737,0,5.263157895,0,5,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,26,68,79.73553719,28.1037111,89.16,75.902548,24,48,51.63636364,25.32889259,69.47,53.96848,5.166666667,4.25,1.666666667,1.25,2,7,2.645751311,2.890371758,1.35483871,1.511209391,0.588197464,15,2,1.310313182,4,18.18873053,2.526054707,4.585845777,1,24,3.428571429,0.653061224,2.612244898,10,26,6,1.378783493,4,2.285714286,4,2.285714286,174,289,114,265,6.786218125,38,48,6.675324675,2.784001567,5.675324675,2.654873479,0.547619048,0.19047619,0.095238095,0,1.37962963,3.254166667,6.680555556,11.32250989,17.5,2.5,4.956403471,3.404799764,0.070637856,-0.040895839,0.125955654,0.017766811,8,2.578755647,0.887089618,0.000849145,-0.110482124,0.247416362,0.000364376,21,1.378783493,1.945910149,3.295836866,3.891820298,4.65396035,5.262690189,6.033086222,6.64509097,7.417580402,8.030409562,8.803424212,2.564949357,0,3.713572067,0,4.976733742,0,6.300785795,0,7.655864018,1.945910149,2.079441542,1.609437912,1.609437912,0,0,0,0,0,0,2.079441542,2.302585093,1.791759469,1.945910149,0,0,0,0,0,0,9.46962297,3.36729583,3.555348061,1.055846662,0.188052232,2.603368154,2.145270715,5.86180732,3.125897658,3.023395961,0.942809042,0.942809042,0,0.837401046,0.520982943,0.431913709,0.23570226,0.23570226,0,5.192705341,2.620626365,2.299986098,0.695705769,0.574914957,0,0.741815049,0.436771061,0.328569443,0.173926442,0.143728739,0,5.86180732,3.125897658,3.023395961,0.942809042,0.942809042,0,19.33273622,1.654068949,21.90865123,2.606723697,3.045939258,2.116654072,2.370974343,80.71062276,22.18129774,1.167436723,1.956066972,4.230388542,1.378783493,2.779475245,41.07740642,203.05865,1.378783493,2.625814584,6,0.857142857,17.65148445,2.001032983,1.23863892,14.72926917,0.875221883,1.20739461,2.67238404,1.167436723,1.878477513,2.970840013,2.970840013,2.747264881,1.95822751,22.18129774,35.69107275,56.44596025,56.44596025,52.19803274,37.20632268,0.27482501,0.44221035,0.699362219,0.699362219,0.64673064,0.46098421,3.08049079,2.36945,1.2770875,1.2770875,1.500662633,2.289700004,0.27482501,0.44221035,0.699362219,0.699362219,0.64673064,0.46098421,18.75538595,3.414213562,0.487744795,0.373303478,2,0.285714286,4,6.828427125,0.975489589,2.772588722,2.765108219,2.5,0.357142857,0.278558006,0.5,0.071428571,-0.210720997,5.560090548,0.79429865,0.521557305,48,2.285714286,13.71428571,11.83333333,0.492203862,0.082033977,2.953223175,4.521788577,14.17597107,2.025138724,0.806086973,14.17597107,2.025138724,21.64007268,28.35194213,4.050277447,8.732949524,14.17597291,5.59471138,7.788626066,10.68118266,13.2162926,15.93039272,2.426262835,0.346608976,0.269456492,0,0,0,5.739236321,0.819890903,0.531197875,48,-6.426891805,2.132600461,0,12,1.714285714,0.755426872,4.414213562,0.630601937,4.14626437,9.435385883,1.347912269,5.82008293,4.927315195,3.663561646,4.976733742,6.368187186,7.788626066,9.222170124,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.893113616,11.82718272,5.981268914,0.996878152,35.88761348,1.222681352,1.816496581,0.259499512,0.181464209,1,0.142857143,2,3.632993162,0.518999023,1.466337069,2.28143274,1.466337069,0,1.358123484,0,1.278874112,2.562948829,0.366135547,0.286117955,0.562948829,0.080421261,-0.174671756,5.561848899,0.794549843,0.52165343,8.444444444,0.402116402,2.412698413,2.248851359,0.37480856,13.49310816,2.136940824,3.815288462,0.545041209,0.407069165,2.606803347,0.372400478,4.261858866,7.630576925,1.090082418,3.282657138,3.027112858,2.639415511,2.603311063,4.094262329,4.675754999,5.840902485,2.447753146,0.349679021,0.272137332,0.351391722,0.050198817,-0.317945932,5.563998554,0.794856936,0.521770906,48,2.285714286,13.71428571,11.83333333,0.492203862,0.082033977,2.953223175,4.521788577,14.17597107,2.025138724,0.806086973,14.17597107,2.025138724,21.64007268,28.35194213,4.050277447,8.732949524,14.17597291,5.59471138,7.788626066,10.68118266,13.2162926,15.93039272,2.426262835,0.346608976,0.269456492,0,0,0,5.739236321,0.819890903,0.531197875,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,44.5,1.589285714,12.67857143,14.38485735,0.528133809,0.088022301,3.168802852,4.400027701,25.96314285,13.10657143,1.872367347,0.782242369,13.10657143,1.872367347,20.3329338,26.14171428,3.734530612,8.087861145,13.10657758,0.223143551,5.454252168,7.528209951,10.38174844,12.81313161,15.46640683,2.4526457,0.350377957,0.272744371,0.068013005,0.009716144,-0.817185628,5.725615498,0.817945071,0.530475526,44.5012,1.589328571,12.679,14.38170258,0.528116397,0.088019399,3.16869838,4.400078348,25.96457826,13.10698913,1.872427018,0.782252057,13.10698913,1.872427018,20.3335127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alkanone,C6H12O,565-69-5,11265,PubChem,,30.7,100.18,5.272631579,9.8756,18.6289,11.0229,21.7006,0.519768421,0.980468421,0.580152632,1.142136842,0.285714286,19,7,4,18,6,1,7,2,0.111111111,1,0,0,12,6,0,1,0,0,0,0,0,0,0,0,1,0,63.15789474,31.57894737,0,5.263157895,0,5,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,26,68,79.73553719,28.1037111,91,75.254912,26,52,55.63636364,27.99333888,76.11,56.446384,5.166666667,4.25,1.777777778,1.416666667,2,7,2.645751311,2.890371758,1.35483871,1.511209391,0.588197464,15,2,1.354079995,6,17.8730874,2.390457219,4.503855938,1,23,3.285714286,0.612244898,2.408163265,9,29,8,1.448815636,4,2.19047619,4,2.19047619,168,273,106,233,6.66900067,36,46,6.675324675,2.784001567,2.380130167,2.654873479,0.557142857,0.304761905,0.05952381,0,1.319444444,3.511574074,7.023148148,12.18938907,17.5,2.5,4.956403471,3.398141088,0.070637856,-0.040895839,0.126121826,0.017672557,8,2.585571719,0.897277662,0.000860648,-0.110271788,0.246577436,0.000374291,21,1.378783493,1.945910149,3.295836866,3.970291914,4.691347882,5.398162702,6.120297419,6.834108739,7.556427969,8.272059622,8.994296558,2.564949357,0,3.713572067,0,5.056245805,0,6.463029457,0,7.890956716,1.945910149,2.079441542,1.945910149,1.098612289,0,0,0,0,0,0,2.079441542,2.302585093,2.302585093,1.098612289,0,0,0,0,0,0,9.660268837,3.36729583,3.555348061,1.055846662,0.188052232,2.60307797,2.150292971,5.86180732,3.180739213,2.629549111,1.782021738,0.471404521,0,0.837401046,0.530123202,0.375649873,0.297003623,0.23570226,0,5.192705341,2.70815999,1.974566772,0.992412263,0.40824829,0,0.741815049,0.451359998,0.282080967,0.165402044,0.204124145,0,5.86180732,3.180739213,2.629549111,1.782021738,0.471404521,0,19.66178554,1.627173667,22.38932109,2.694257322,3.113264398,2.190321638,2.483659956,80.71062276,22.18129774,1.167436723,1.884172925,4.251185325,2.521640636,2.776119445,39.56763142,195.554525,1.378783493,2.625814584,6,0.857142857,17.65148445,1.970221248,1.245613437,14.55360052,0.96551028,1.270647212,3.253112643,1.167436723,1.733483434,2.535857776,2.970840013,2.747264881,2.076539484,22.18129774,32.93618524,48.18129774,56.44596025,52.19803274,39.4542502,0.27482501,0.408077452,0.596963523,0.699362219,0.64673064,0.488835903,3.08049079,2.51444408,1.712069738,1.2770875,1.500662633,2.171388029,0.27482501,0.408077452,0.596963523,0.699362219,0.64673064,0.488835903,19.08443528,3.831493958,0.54735628,0.408357701,2.05288084,0.293268691,4.10576168,7.662987915,1.094712559,2.890371758,2.766317013,2.45983181,0.351404544,0.273633789,0.093061608,0.013294515,-0.721860612,5.552761685,0.793251669,0.521156324,46,2.19047619,13.14285714,12,0.524034701,0.087339117,3.144208207,4.430816799,13.63462203,1.947803147,0.794250172,13.63462203,1.947803147,19.85726871,27.26924405,3.895606293,8.706490362,13.63462517,5.484796933,7.721791777,10.49573647,13.04245616,15.68479403,2.350941918,0.335848845,0.25986933,0.048608437,0.006944062,-0.919301837,5.750849644,0.821549949,0.531812409,46,-6.159104646,1.776169319,0,12,1.714285714,0.755426872,4.628651282,0.661235897,4.306931632,9.710395916,1.387199417,5.80814249,4.971323474,3.663561646,4.976733742,6.381816017,7.833203949,9.311722986,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.908782632,12.05985573,5.968049074,0.994674846,35.80829445,1.246819195,2.201850425,0.314550061,0.239951469,1,0.142857143,2,4.40370085,0.629100121,1.563975538,2.292173574,1.516347489,0,1.325862622,0,1.230370276,2.562948829,0.366135547,0.286117955,0.169101979,0.024157426,-0.540295917,5.555979679,0.793711383,0.521332454,8.541666667,0.406746032,2.44047619,2.23350476,0.372250793,13.40102856,2.143651097,3.87952078,0.554217254,0.412144658,2.672307736,0.381758248,4.365973724,7.759041561,1.108434509,3.143621215,3.04993562,2.641823012,2.639682341,4.150095046,4.80007426,5.972970241,2.406659833,0.343808548,0.266990297,0.061390182,0.008770026,-0.848330755,5.556666987,0.79380957,0.521370059,46,2.19047619,13.14285714,12,0.524034701,0.087339117,3.144208207,4.430816799,13.63462203,1.947803147,0.794250172,13.63462203,1.947803147,19.85726871,27.26924405,3.895606293,8.706490362,13.63462517,5.484796933,7.721791777,10.49573647,13.04245616,15.68479403,2.350941918,0.335848845,0.25986933,0.048608437,0.006944062,-0.919301837,5.750849644,0.821549949,0.531812409,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,42.5,1.517857143,12.10714286,14.63437002,0.567501699,0.094583616,3.405010193,4.313521925,25.02490021,12.6374501,1.805350015,0.771161618,12.6374501,1.805350015,18.84162152,25.20347164,3.600495948,8.063131807,12.63745986,0.223143551,5.350672597,7.470308712,10.2053365,12.653946,15.23397362,2.323830709,0.331975816,0.256343141,0.03786306,0.005409009,-0.995249002,5.750979372,0.821568482,0.531819267,42.5012,1.5179,12.10757143,14.63116157,0.567480492,0.094580082,3.40488295,4.313569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alkanone,C6H12O,565-61-7,11262,PubChem,,29,100.18,5.272631579,9.8756,18.6289,11.0229,21.7006,0.519768421,0.980468421,0.580152632,1.142136842,0.285714286,19,7,4,18,6,1,7,2,0.111111111,1,0,0,12,6,0,1,0,0,0,0,0,0,0,0,1,0,63.15789474,31.57894737,0,5.263157895,0,5,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,26,68,79.73553719,28.1037111,90.5,75.363288,26,51,54.63636364,27.66111574,74.07,55.879184,5.166666667,4.25,1.777777778,1.375,2,7,2.645751311,2.890371758,1.35483871,1.511209391,0.588197464,15,2,1.34275231,6,17.8730874,2.390457219,4.503855938,1,23,3.285714286,0.612244898,2.408163265,9,29,8,1.448815636,4,2.19047619,4,2.19047619,168,278,106,245,6.66900067,36,46,6.675324675,2.784001567,2.380130167,2.654873479,0.557142857,0.304761905,0.05952381,0,1.372685185,3.374768519,6.789351852,11.83892591,17.5,2.5,4.956403471,3.408142459,0.070637856,-0.040895839,0.125873226,0.017813751,8,2.58170456,0.89267475,0.00085411,-0.11039099,0.246965044,0.000369677,21,1.378783493,1.945910149,3.295836866,3.970291914,4.691347882,5.398162702,6.120297419,6.834108739,7.556427969,8.272059622,8.994296558,2.564949357,0,3.713572067,0,5.056245805,0,6.463029457,0,7.890956716,1.945910149,2.079441542,1.945910149,1.098612289,0,0,0,0,0,0,2.079441542,2.302585093,2.197224577,1.386294361,0,0,0,0,0,0,9.660268837,3.36729583,3.555348061,1.055846662,0.188052232,2.60307797,2.150292971,5.86180732,3.180739213,2.629549111,1.782021738,0.471404521,0,0.837401046,0.530123202,0.375649873,0.297003623,0.23570226,0,5.192705341,2.685504621,1.919946271,1.306356179,0.287457479,0,0.741815049,0.447584103,0.274278039,0.21772603,0.143728739,0,5.86180732,3.180739213,2.629549111,1.782021738,0.471404521,0,19.66178554,1.627173667,22.38932109,2.671601953,3.104613364,2.168030877,2.4575601,80.71062276,22.18129774,1.167436723,1.884172925,4.251185325,2.521640636,2.776119445,39.56763142,195.554525,1.378783493,2.625814584,7,1,19.65148445,1.970221248,1.245613437,14.55360052,0.96551028,1.270647212,3.253112643,1.167436723,1.878477513,3.076103171,3.391892645,3.168317512,2.274016983,22.18129774,35.69107275,58.44596025,64.44596025,60.19803274,43.20632268,0.27482501,0.44221035,0.724142105,0.798481762,0.745850183,0.535323867,3.08049079,2.36945,1.171824342,0.856034869,1.079610001,1.97391053,0.27482501,0.44221035,0.724142105,0.798481762,0.745850183,0.535323867,19.08443528,3.831493958,0.54735628,0.408357701,2.05288084,0.293268691,4.10576168,7.662987915,1.094712559,2.890371758,2.766317013,2.45983181,0.351404544,0.273633789,0.093061608,0.013294515,-0.721860612,5.552761685,0.793251669,0.521156324,46,2.19047619,13.14285714,12,0.524034701,0.087339117,3.144208207,4.430816799,13.63462203,1.947803147,0.794250172,13.63462203,1.947803147,19.85726871,27.26924405,3.895606293,8.706490362,13.63462517,5.484796933,7.721791777,10.49573647,13.04245616,15.68479403,2.350941918,0.335848845,0.25986933,0.048608437,0.006944062,-0.919301837,5.750849644,0.821549949,0.531812409,46,-6.159104646,1.776169319,0,12,1.714285714,0.755426872,4.628651282,0.661235897,4.306931632,9.710395916,1.387199417,5.80814249,4.971323474,3.663561646,4.976733742,6.381816017,7.833203949,9.311722986,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.908782632,12.05985573,5.968049074,0.994674846,35.80829445,1.246819195,2.201850425,0.314550061,0.239951469,1,0.142857143,2,4.40370085,0.629100121,1.563975538,2.292173574,1.516347489,0,1.325862622,0,1.230370276,2.562948829,0.366135547,0.286117955,0.169101979,0.024157426,-0.540295917,5.555979679,0.793711383,0.521332454,8.541666667,0.406746032,2.44047619,2.23350476,0.372250793,13.40102856,2.143651097,3.87952078,0.554217254,0.412144658,2.672307736,0.381758248,4.365973724,7.759041561,1.108434509,3.143621215,3.04993562,2.641823012,2.639682341,4.150095046,4.80007426,5.972970241,2.406659833,0.343808548,0.266990297,0.061390182,0.008770026,-0.848330755,5.556666987,0.79380957,0.521370059,46,2.19047619,13.14285714,12,0.524034701,0.087339117,3.144208207,4.430816799,13.63462203,1.947803147,0.794250172,13.63462203,1.947803147,19.85726871,27.26924405,3.895606293,8.706490362,13.63462517,5.484796933,7.721791777,10.49573647,13.04245616,15.68479403,2.350941918,0.335848845,0.25986933,0.048608437,0.006944062,-0.919301837,5.750849644,0.821549949,0.531812409,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,42.5,1.517857143,12.10714286,14.59294701,0.565515571,0.094252595,3.393093426,4.30512028,24.92301184,12.58650592,1.798072274,0.769933629,12.58650592,1.798072274,18.60695904,25.10158327,3.585940466,8.060283462,12.58651618,0.223143551,5.338739281,7.465006667,10.18470947,12.63700883,15.20782599,2.290802482,0.327257497,0.251991351,0.127249147,0.01817845,-0.626741584,5.756701613,0.822385945,0.532121603,42.5012,1.5179,12.10757143,14.58976541,0.565495327,0.094249221,3.392971961,4.305171919,24.92442949,12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compounds,C6H14O2,105-57-7,7765,PubChem,,22.4,118.2,5.372727273,11.1172,21.8398,12.2388,25.3252,0.505327273,0.992718182,0.556309091,1.151145455,0.25,22,8,3,21,7,0,7,4,0.19047619,0,0,0,14,6,0,2,0,0,0,0,0,0,0,0,2,0,63.63636364,27.27272727,0,9.090909091,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,92,115.4710744,35.30669627,122.14,95.721696,27,67,76.09090909,32.31557036,103.07,70.65432,5.333333333,4.666666667,2.166666667,1.611111111,1,7,2.645751311,3.871201011,1.5,1.622389604,0.508249709,18,1,1.302993695,6,23.25211835,3.017804311,4.720931985,1,37,4.625,0.875,3.3125,13,46,12,1.905639062,6,2.678571429,6,2.678571429,271,431,195,411,8.219930079,61,75,7.922077922,5.066543213,4.922077922,5.017193766,0.491666667,0.255242674,0.125886613,0.031986532,1.37037037,1.56,6.24,8.029544714,17.33333333,2.166666667,5.689383877,3.565764635,0.056683864,0.022214747,-0.07874601,0.006475538,7.666666667,2.945274023,0.960998894,0.000321107,0.06284044,-0.148236895,7.5819E-05,18,1.905639062,2.079441542,3.36729583,4.007333185,4.709530201,5.370638028,6.084499413,6.751101469,7.467942332,8.135932772,8.853522561,2.708050201,0,3.761200116,0,4.990432587,0,6.304448802,0,7.656810091,2.079441542,2.079441542,1.945910149,1.791759469,1.098612289,0.693147181,0,0,0,0,2.079441542,2.079441542,1.945910149,1.791759469,1.098612289,0.693147181,0,0,0,0,9.537916008,3.610917913,3.610917913,1,0,2.751012246,2.245682124,6.405777394,3.808060412,2.682521985,1.562948829,1.129936127,0.288675135,0.800722174,0.54400863,0.383217426,0.260491471,0.225987225,0.144337567,5.808060412,3.040318622,1.478313168,0.80274943,0.517528619,0.096225045,0.726007552,0.434331232,0.211187595,0.133791572,0.103505724,0.048112522,6.405777394,3.808060412,2.682521985,1.562948829,1.129936127,0.288675135,24.66485906,1.878752807,26.51036701,2.968889104,3.496823185,2.389778356,2.807745766,98.10749561,27.2947517,1.240670532,2.363698337,4.612040278,1.905639062,2.964571277,66.18355343,345.9030208,1.405639062,2.896291529,5,0.625,18,2.370864648,1.294033502,18.5203906,0.755738641,1.102614844,2.164971808,1.240670532,1.638619964,2.526280482,2.901946959,2.901946959,2.699245522,27.2947517,36.0496392,55.5781706,63.8428331,63.8428331,59.38340148,0.278212705,0.367450407,0.566502796,0.650743684,0.650743684,0.605289138,3.218761087,2.820811655,1.933151137,1.557484659,1.557484659,1.760186097,0.282463768,0.373065015,0.575158906,0.660686986,0.660686986,0.614537899,26.65642289,4.414213562,0.551776695,0.515882673,2,0.25,4,8.828427125,1.103553391,3.433987204,2.888532276,2.598076211,0.324759526,0.331721509,0.288675135,0.036084392,-0.431672498,6.559884359,0.819985545,0.653516947,75,2.678571429,18.75,14.31666667,0.41708752,0.059583931,2.919612641,5.111987788,19.54202305,2.442752881,1.032775617,19.54202305,2.442752881,29.63985773,39.0840461,4.885505763,10.32301992,19.54202306,6.23636959,8.754160749,11.96172461,14.82466521,17.85439454,2.341896018,0.292737002,0.295654087,0,0,0,6.721964274,0.840245534,0.66199696,75,-8.698792047,2.52445867,0,14,1.75,0.916902429,4.362339833,0.545292479,4.164277569,10.37020798,1.296275997,6.828712072,4.900527807,3.761200116,4.990432587,6.304448802,7.666221926,9.058703197,2.828427125,0.353553391,0.361235995,2.828427125,0.353553391,0.361235995,6.599663291,0.824957911,0.655617423,0.952015103,13.45945742,6.967160088,0.995308584,48.77012062,1.383130245,2.511522628,0.313940329,0.319949676,1,0.125,2,5.023045256,0.627880657,1.7847908,2.426178829,1.673976434,0,1.497885427,0,1.383396648,2.776551668,0.347068958,0.354804608,0.338873676,0.042359209,-0.375969733,6.565312235,0.820664029,0.653804308,9.836944444,0.351319444,2.459236111,2.716495952,0.38807085,19.01547166,2.270682647,4.657984927,0.582248116,0.534558463,2.659874711,0.332484339,4.316355344,9.315969854,1.164496232,3.756346352,3.146791307,2.776963754,2.659716726,4.190658071,4.790273468,5.96004991,2.544377903,0.318047238,0.324465292,0.200902294,0.025112787,-0.557612084,6.563389909,0.820423739,0.653702564,75,2.678571429,18.75,14.31666667,0.41708752,0.059583931,2.919612641,5.111987788,19.54202305,2.442752881,1.032775617,19.54202305,2.442752881,29.63985773,39.0840461,4.885505763,10.32301992,19.54202306,6.23636959,8.754160749,11.96172461,14.82466521,17.85439454,2.341896018,0.292737002,0.295654087,0,0,0,6.721964274,0.840245534,0.66199696,28,1,7,28,1,0.142857143,7,3.36729583,7,0.875,0.676078432,7,0.875,8,14,1.75,6.647688374,7.003254818,4.043051268,5.82008293,7.786967003,9.729193687,11.67552889,0,0,0,0,0,0,0,0,0,62,1.722222222,15.4375,17.82808303,0.51254986,0.073221409,3.58784902,4.766970107,31.86903667,16.18451833,2.023064792,0.967279823,16.18451833,2.023064792,24.01478799,32.24403667,4.030504583,9.238599787,16.18451866,0.405465108,5.838094812,8.216451208,11.19237762,13.89273633,16.71722061,2.315080935,0.289385117,0.291652943,0,0,0,6.731433328,0.841429166,0.662486039,62.0312,1.723088889,15.44545,17.81756075,0.51226783,0.073181119,3.58587481,4.767865623,31.88628665,16.19254333,2.024067916,0.967452054,16.19254333,2.024067916,2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+UMLYSI0367,L,methyl 2-methylpropanoate,CC(C)C(=O)OC,saturated 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compounds,C5H12O2,53778-73-7,40895,PubChem,,19.8,104.17,5.482631579,9.5904,18.9562,10.4774,21.91,0.504757895,0.997694737,0.551442105,1.153157895,0.285714286,19,7,3,18,6,0,6,3,0.166666667,0,0,0,12,5,0,2,0,0,0,0,0,0,0,0,2,0,63.15789474,26.31578947,0,10.52631579,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24,80,99.88521579,28.31779443,101.16,86.3241,23,47,52,27.09704999,72.57,53.9118,4.833333333,3.7,1.916666667,1.15,1,6,2.449489743,3.17805383,1.448275862,1.574610106,0.56094383,16,1,1.239362838,5,18.44734799,2.654735212,4.126706124,0.666666667,28,4,0.571428571,2.612244898,10,30,9,1.556656707,5,2.380952381,5,2.380952381,181,321,122,354,6.946161136,45,50,6.922077922,4.090937571,3.760188211,4.045398377,0.5,0.267176871,0.095044836,0.021915584,1.606481481,2.732916667,7.754814815,10.06583005,17.83333333,2.547619048,4.856002902,3.159517476,0.061089529,-0.043229605,0.13503849,0.013284685,8.047619048,2.568402878,0.863068587,0.000821327,-0.1110028,0.251045289,0.000324427,17,1.556656707,1.945910149,3.218875825,3.850147602,4.532599493,5.187385806,5.877735782,6.538139824,7.230563153,7.892452044,8.58541243,2.564949357,0,3.610917913,0,4.844187086,0,6.150602768,0,7.486052618,1.945910149,1.945910149,1.791759469,1.386294361,0.693147181,0,0,0,0,0,1.945910149,1.945910149,1.791759469,1.386294361,0.693147181,0,0,0,0,0,9.28804188,3.36729583,3.36729583,1,0,2.614865278,2.142011297,5.698670613,3.308060412,2.302095141,1.478397839,0.696923425,0.204124145,0.814095802,0.551343402,0.383682523,0.295679568,0.232307808,0.204124145,4.847025718,2.478725677,1.517413598,0.830302717,0.31023786,0.11785113,0.692432245,0.413120946,0.252902266,0.166060543,0.10341262,0.11785113,5.698670613,3.308060412,2.302095141,1.478397839,0.696923425,0.204124145,21.63281953,1.723209833,21.19445472,2.413673362,3.076644828,1.982343858,2.282526329,80.71062276,24.08143227,1.267443804,2.135932757,4.21617463,2.807354922,2.773312733,44.85458789,210.8087315,1.448815636,2.688721876,7,1,19.65148445,2.205114616,1.236303072,14.74169749,0.825971181,1.158812582,2.459659311,1.267443804,2.245098682,3.536886724,3.536886724,3.536886724,3.089736459,24.08143227,42.65687495,67.20084775,67.20084775,67.20084775,58.70499272,0.298367569,0.528516241,0.83261466,0.83261466,0.83261466,0.727351502,2.98048371,2.002828832,0.71104079,0.71104079,0.71104079,1.158191054,0.303948825,0.538402653,0.848189529,0.848189529,0.848189529,0.740957321,19.84836247,3.939231012,0.562747287,0.416788015,1.969615506,0.281373644,3.939231012,7.878462024,1.125494575,2.995732274,2.752839537,2.458386904,0.351198129,0.273455164,0.215080985,0.030725855,-0.467178589,5.570942974,0.795848996,0.522150099,50,2.380952381,14.28571429,11.61666667,0.471969994,0.078661666,2.831819966,4.605170186,14.86357869,2.123368384,0.820486371,14.86357869,2.123368384,22.55648517,29.72715738,4.246736768,8.823206221,14.86357965,5.693732139,7.932003152,10.86759655,13.45609042,16.21265509,2.394401389,0.342057341,0.265437869,0.059016506,0.008430929,-0.860318553,5.72615888,0.818022697,0.530504376,50,-6.694678963,2.195077851,0,12,1.714285714,0.755426872,4.334200532,0.619171505,4.073877841,9.130266124,1.304323732,5.869296913,4.765742035,3.610917913,4.844187086,6.167516491,7.540621529,8.944158831,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.945160118,11.45945742,5.969002809,0.994833801,35.81401685,1.284362278,2.316496581,0.330928083,0.255382164,1,0.142857143,2,4.632993162,0.661856166,1.642227735,2.30716955,1.575536361,0,1.389760569,0,1.287534953,2.590770275,0.370110039,0.289400234,0.196923425,0.028131918,-0.493991833,5.574879675,0.796411382,0.522364849,8.283055556,0.394431217,2.366587302,2.393986775,0.398997796,14.36392065,2.124241775,4.017017002,0.573859572,0.422732569,2.55690369,0.365271956,4.197913496,8.034034004,1.147719143,3.247582134,2.99553753,2.631895624,2.499241349,4.013658965,4.571570731,5.722750231,2.411173456,0.344453351,0.267559918,0.153043803,0.0218634,-0.570628976,5.574272268,0.79632461,0.522331724,50,2.380952381,14.28571429,11.61666667,0.471969994,0.078661666,2.831819966,4.605170186,14.86357869,2.123368384,0.820486371,14.86357869,2.123368384,22.55648517,29.72715738,4.246736768,8.823206221,14.86357965,5.693732139,7.932003152,10.86759655,13.45609042,16.21265509,2.394401389,0.342057341,0.265437869,0.059016506,0.008430929,-0.860318553,5.72615888,0.818022697,0.530504376,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,45,1.607142857,12.78571429,13.82385392,0.524251734,0.087375289,3.145510406,4.412798293,26.04604301,13.27302151,1.896145929,0.786078859,13.27302151,1.896145929,20.23152726,26.40318587,3.771883696,8.025873991,13.2730269,0.405465108,5.467532576,7.5879122,10.41812115,12.88769586,15.53506769,2.371386667,0.338769524,0.262501662,0.02878882,0.004112689,-1.078543298,5.726309447,0.818044207,0.530512369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compounds,C5H12O2,7778-85-0,24509,PubChem,,18.6,104.17,5.482631579,9.5904,18.9562,10.4774,21.91,0.504757895,0.997694737,0.551442105,1.153157895,0.285714286,19,7,3,18,6,0,6,3,0.166666667,0,0,0,12,5,0,2,0,0,0,0,0,0,0,0,2,0,63.15789474,26.31578947,0,10.52631579,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24,88,111.4710744,29.09261294,111.08,93.235844,23,51,57.36363636,27.76149628,79.93,61.04772,4.833333333,4.166666667,1.916666667,0.972222222,1,6,2.449489743,3.17805383,1.448275862,1.574610106,0.56094383,16,1,1.163236268,5,18.44734799,2.654735212,4.126706124,0.666666667,28,4,0.571428571,2.612244898,10,30,9,1.556656707,5,2.380952381,5,2.380952381,181,317,122,338,6.946161136,45,50,6.922077922,4.090937571,3.760188211,4.045398377,0.5,0.267176871,0.095044836,0.021915584,1.106481481,1.365555556,5.270092593,6.456478629,15.83333333,2.261904762,4.872887296,2.974753993,0.068992871,-0.041275029,0.138125656,0.012034851,7.761904762,2.556067025,0.809047625,0.000832115,-0.110798806,0.255581738,0.00028059,17,1.556656707,1.945910149,3.218875825,3.850147602,4.532599493,5.187385806,5.877735782,6.538139824,7.230563153,7.892452044,8.58541243,2.564949357,0,3.610917913,0,4.844187086,0,6.150602768,0,7.486052618,1.945910149,1.945910149,1.791759469,1.386294361,0.693147181,0,0,0,0,0,1.945910149,1.945910149,1.791759469,1.386294361,0.693147181,0,0,0,0,0,9.28804188,3.36729583,3.36729583,1,0,2.614865278,2.142011297,5.698670613,3.308060412,2.302095141,1.478397839,0.696923425,0.204124145,0.814095802,0.551343402,0.383682523,0.295679568,0.232307808,0.204124145,5.100953631,2.326472536,1.501661279,0.803743642,0.30274943,0.068041382,0.728707662,0.387745423,0.25027688,0.160748728,0.100916477,0.068041382,5.698670613,3.308060412,2.302095141,1.478397839,0.696923425,0.204124145,22.17082433,1.723209833,21.19445472,2.235147106,2.982165584,1.80305083,2.050455312,80.71062276,24.08143227,1.267443804,2.135932757,4.21617463,2.807354922,2.773312733,44.85458789,210.8087315,1.448815636,2.688721876,7,1,19.65148445,2.205114616,1.236303072,14.74169749,0.825971181,1.158812582,2.459659311,1.267443804,1.773214355,2.970840013,3.076103171,3.391892645,2.94474238,24.08143227,33.69107275,56.44596025,58.44596025,64.44596025,55.95010522,0.298367569,0.417430464,0.699362219,0.724142105,0.798481762,0.693218604,2.98048371,2.474713158,1.2770875,1.171824342,0.856034869,1.303185133,0.303948825,0.425238908,0.712444471,0.737687889,0.813418141,0.706185934,19.84836247,3.939231012,0.562747287,0.416788015,1.969615506,0.281373644,3.939231012,7.878462024,1.125494575,2.995732274,2.752839537,2.458386904,0.351198129,0.273455164,0.215080985,0.030725855,-0.467178589,5.570942974,0.795848996,0.522150099,50,2.380952381,14.28571429,11.61666667,0.471969994,0.078661666,2.831819966,4.605170186,14.86357869,2.123368384,0.820486371,14.86357869,2.123368384,22.55648517,29.72715738,4.246736768,8.823206221,14.86357965,5.693732139,7.932003152,10.86759655,13.45609042,16.21265509,2.394401389,0.342057341,0.265437869,0.059016506,0.008430929,-0.860318553,5.72615888,0.818022697,0.530504376,50,-6.694678963,2.195077851,0,12,1.714285714,0.755426872,4.334200532,0.619171505,4.073877841,9.130266124,1.304323732,5.869296913,4.765742035,3.610917913,4.844187086,6.167516491,7.540621529,8.944158831,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.945160118,11.45945742,5.969002809,0.994833801,35.81401685,1.284362278,2.316496581,0.330928083,0.255382164,1,0.142857143,2,4.632993162,0.661856166,1.642227735,2.30716955,1.575536361,0,1.389760569,0,1.287534953,2.590770275,0.370110039,0.289400234,0.196923425,0.028131918,-0.493991833,5.574879675,0.796411382,0.522364849,8.283055556,0.394431217,2.366587302,2.393986775,0.398997796,14.36392065,2.124241775,4.017017002,0.573859572,0.422732569,2.55690369,0.365271956,4.197913496,8.034034004,1.147719143,3.247582134,2.99553753,2.631895624,2.499241349,4.013658965,4.571570731,5.722750231,2.411173456,0.344453351,0.267559918,0.153043803,0.0218634,-0.570628976,5.574272268,0.79632461,0.522331724,50,2.380952381,14.28571429,11.61666667,0.471969994,0.078661666,2.831819966,4.605170186,14.86357869,2.123368384,0.820486371,14.86357869,2.123368384,22.55648517,29.72715738,4.246736768,8.823206221,14.86357965,5.693732139,7.932003152,10.86759655,13.45609042,16.21265509,2.394401389,0.342057341,0.265437869,0.059016506,0.008430929,-0.860318553,5.72615888,0.818022697,0.530504376,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,42.5,1.517857143,12.07142857,14.49568765,0.554215417,0.092369236,3.325292504,4.307437778,24.48025655,12.49012828,1.784304039,0.767596829,12.49012828,1.784304039,18.93917724,24.83739941,3.548199915,7.755593172,12.49014051,0.405465108,5.341735987,7.413178838,10.1707057,12.58678975,15.16850621,2.462258389,0.351751198,0.273933538,0.052554326,0.007507761,-0.89557407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compounds,C5H12O2,462-95-3,10024,PubChem,,18.5,104.17,5.482631579,9.5904,18.9562,10.4774,21.91,0.504757895,0.997694737,0.551442105,1.153157895,0.285714286,19,7,2,18,6,0,6,4,0.222222222,0,0,0,12,5,0,2,0,0,0,0,0,0,0,0,2,0,63.15789474,26.31578947,0,10.52631579,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22,86,109.4710744,27.97780368,109.62,89.9,20,52,59.27272727,24.65112406,82,58.3376,4.5,3.833333333,2,1.333333333,0,5,2.236067977,3.465735903,1.555555556,1.640670712,0.537158271,16,0,1.28445705,4,19.24902768,3.055050463,3.627465013,1,33,4.714285714,0.897959184,2.857142857,12,28,9,1.950212065,6,2.666666667,6,2.666666667,202,338,146,338,7.297142506,54,56,6.922077922,5.922077922,5.922077922,5.856154977,0.452380952,0.219387755,0.096598639,0.042857143,1.0625,1.351666667,5.406666667,4.623760096,15.5,2.214285714,4.932748273,2.90044366,0.074886278,-0.039955033,0.139740753,0.011428571,7.714285714,2.565966074,0.797584224,0.000853715,-0.110398222,0.256720966,0.000270545,13,1.950212065,1.945910149,3.135494216,3.713572067,4.317488114,4.919980926,5.533389489,6.142037406,6.758094504,7.368339686,7.985143931,2.564949357,0,3.496507561,0,4.574710979,0,5.720311777,0,6.900730664,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,8.761236807,3.36729583,3.36729583,1,0,2.627114062,2.132338507,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,0.790790558,0.569035594,0.412132034,0.301776695,0.225592232,0.176776695,4.937816924,2.568914101,1.10344969,0.574914957,0.225592232,0.11785113,0.705402418,0.42815235,0.220689938,0.143728739,0.075197411,0.058925565,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,22.48528137,1.840004522,19.78207923,2.490268736,3.102045534,2.034178074,2.36877908,80.71062276,24.08143227,1.267443804,2.398302995,4.168986958,1.950212065,2.774244972,50.3643629,233.4632697,0.863120569,2.751629167,4,0.571428571,13.65148445,2.440007983,1.22222776,15.01828953,0.666081376,1.019498601,1.729713704,1.267443804,1.667951197,2.589518303,2.589518303,2.589518303,2.36594317,24.08143227,31.69107275,49.20084775,49.20084775,49.20084775,44.95292024,0.298367569,0.392650579,0.60959569,0.60959569,0.60959569,0.556964111,2.98048371,2.579976316,1.658409211,1.658409211,1.658409211,1.881984343,0.303948825,0.399995491,0.620998771,0.620998771,0.620998771,0.567382667,20.48528137,4.027339492,0.575334213,0.423512769,1.847759065,0.263965581,3.69551813,8.054678984,1.150668426,3.091042453,2.73919265,2.513669746,0.359095678,0.280215753,0.099456184,0.014208026,-0.701657747,5.592530245,0.798932892,0.523325839,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,56,-7.498040439,2.885095623,0,12,1.714285714,0.755426872,3.801937736,0.543133962,3.603875472,8.702204129,1.243172018,5.934894196,4.522970604,3.555348061,4.691347882,5.894402834,7.133295955,8.394121194,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.975489589,10.82842712,5.985143843,0.997523974,35.91086306,1.310251729,2.366025404,0.338003629,0.261813582,1,0.142857143,2,4.732050808,0.676007258,1.681758574,2.316436311,1.609437912,0,1.446918983,0,1.354545663,2.618591722,0.374084532,0.292647453,0.169101979,0.024157426,-0.540295917,5.594918316,0.799274045,0.523455625,7.989722222,0.380462963,2.282777778,2.564019422,0.42733657,15.38411653,2.102137974,4.119483043,0.588497578,0.430389904,2.400399559,0.342914223,3.963351092,8.238966087,1.176995155,3.346584212,2.935808684,2.621413019,2.328341703,3.846629402,4.257734494,5.383121673,2.490590677,0.355798668,0.277411651,0.067967869,0.009709696,-0.817387444,5.593895954,0.799127993,0.523400069,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,45.5,1.625,12.92857143,14.31379731,0.510049463,0.085008244,3.060296776,4.468777561,26.55549206,13.52774603,1.932535147,0.791857809,13.52774603,1.932535147,21.35801569,26.91263492,3.844662131,7.855581014,13.5277505,0.405465108,5.540773229,7.587532225,10.52702358,12.95613961,15.66544984,2.315080935,0.330725848,0.255196325,0,0,0,5.731433328,0.81877619,0.530784271,45.5252,1.6259,12.93594286,14.30424641,0.509742772,0.084957129,3.058456633,4.469760723,26.5715211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compounds,C5H12O2,77-76-9,6495,PubChem,,16.4,104.17,5.482631579,9.5904,18.9562,10.4774,21.91,0.504757895,0.997694737,0.551442105,1.153157895,0.285714286,19,7,4,18,6,0,6,2,0.111111111,0,0,0,12,5,0,2,0,0,0,0,0,0,0,0,2,0,63.15789474,26.31578947,0,10.52631579,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,92,115.4710744,35.30669627,122.7,98.03201296,28,68,77.09090909,33.31557036,106.12,78.901568,5.25,4.583333333,1.75,0.916666667,3,8,2.828427125,2.772588722,1.333333333,1.485994289,0.600561204,16,2,1.112372436,6,17.55030825,2.267786838,4.393619129,1,22,3.142857143,0.489795918,2.204081633,8,32,8,1.378783493,4,2.095238095,4,2.095238095,162,290,98,258,6.544661395,34,44,6.922077922,2.286087597,2.597516519,2.260639497,0.576190476,0.302380952,0.035714286,0,1.666666667,1.364583333,5.458333333,7.018462259,16.25,2.321428571,4.791899283,2.977022898,0.061929799,-0.043010403,0.138800533,0.011777696,7.821428571,2.552084187,0.835548531,0.000817548,-0.111074878,0.253725,0.000297767,21,1.378783493,1.945910149,3.36729583,4.043051268,4.844187086,5.549076085,6.354370041,7.063903961,7.870547845,8.580918882,9.387900612,2.564949357,0,3.80666249,0,5.262690189,0,6.767343125,0,8.282230063,1.945910149,2.197224577,1.945910149,0.693147181,0,0,0,0,0,0,1.945910149,2.197224577,1.945910149,0.693147181,0,0,0,0,0,0,10.00378527,3.36729583,3.36729583,1,0,2.598436492,2.156849666,5.914213562,3.121320344,2.871320344,1.914213562,0.25,0,0.844887652,0.520220057,0.358915043,0.319035594,0.25,0,5.316496581,2.224744871,1.808078205,0.983163248,0.083333333,0,0.759499512,0.370790812,0.226009776,0.163860541,0.083333333,0,5.914213562,3.121320344,2.871320344,1.914213562,0.25,0,20.8492424,1.575449285,23.20776119,2.141328865,2.80610497,1.685146429,1.894841708,80.71062276,24.08143227,1.267443804,1.772338851,4.265583323,1.950212065,2.773989252,37.21911587,187.6856662,1.378783493,2.584962501,4,0.571428571,13.65148445,1.857776861,1.250358386,14.44425022,1.073698468,1.347900316,4.095921172,1.267443804,1.667951197,2.299530145,2.299530145,2.075955012,1.392180799,24.08143227,31.69107275,43.69107275,43.69107275,39.44314523,26.45143518,0.298367569,0.392650579,0.541329893,0.541329893,0.488698314,0.327731769,2.98048371,2.579976316,1.948397369,1.948397369,2.171972501,2.855746715,0.303948825,0.399995491,0.551455996,0.551455996,0.497839892,0.333862311,18.72792206,3.797932652,0.542561807,0.405683081,2.135779205,0.305111315,4.27155841,7.595865304,1.085123615,2.833213344,2.780225452,2.429858132,0.34712259,0.269906642,0.377377163,0.053911023,-0.296257069,5.537520834,0.791074405,0.520320761,44,2.095238095,12.57142857,12.25,0.560072524,0.093345421,3.360435143,4.343805422,13.06981118,1.867115882,0.781388519,13.06981118,1.867115882,18.30587915,26.13962235,3.734231764,8.638702609,13.0698166,5.379897354,7.621195163,10.31164902,12.83989701,15.4262662,1.946497979,0.27807114,0.20247777,0,0,0,5.80804425,0.829720607,0.534820942,44,-5.891317487,1.496019422,0,12,1.714285714,0.755426872,5.164247938,0.737749705,4.782281927,9.733118453,1.390445493,5.771441123,5.327435308,3.713572067,5.129898715,6.665683718,8.256607345,9.873801242,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.891805812,12.48528137,5.960502738,0.993417123,35.76301643,1.234209097,2.207106781,0.315300969,0.240676342,1,0.142857143,2,4.414213562,0.630601937,1.558144618,2.288749508,1.504077397,0,1.287854288,0,1.188224447,2.548547388,0.364078198,0.284404899,0.239146312,0.034163759,-0.434935419,5.542451738,0.79177882,0.520591344,8.729166667,0.415674603,2.494047619,2.150389306,0.358398218,12.90233584,2.157972356,3.84667799,0.549525427,0.409560083,2.76515393,0.39502199,4.507402781,7.693355981,1.099050854,3.075489687,3.097117636,2.650135783,2.747408161,4.257378522,4.978206213,6.169089535,2.362463228,0.337494747,0.26135554,0.269137311,0.038448187,-0.399018263,5.542730567,0.791818652,0.520606638,44,2.095238095,12.57142857,12.25,0.560072524,0.093345421,3.360435143,4.343805422,13.06981118,1.867115882,0.781388519,13.06981118,1.867115882,18.30587915,26.13962235,3.734231764,8.638702609,13.0698166,5.379897354,7.621195163,10.31164902,12.83989701,15.4262662,1.946497979,0.27807114,0.20247777,0,0,0,5.80804425,0.829720607,0.534820942,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,36.5,1.303571429,10.35714286,15.44126984,0.681375961,0.11356266,4.088255768,4.007333185,20.97584908,10.73792454,1.53398922,0.721644243,10.73792454,1.53398922,14.59948004,21.33299194,3.047570277,7.524746719,10.73799053,0.405465108,4.968944867,7.049417588,9.517117675,11.86158979,14.24516135,1.930688427,0.275812632,0.199998535,0,0,0,5.813731848,0.830533121,0.535118498,36.518,1.304214286,10.36245714,15.43167983,0.681020103,0.113503351,4.08612062,4.008194868,20.98812135,10.74346067,1.534780096,0.721800939,10.74346067,1.534780096,14.6083039,21.34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compounds,C5H12O4,1850-14-2,74613,PubChem,,14.8,136.17,6.484285714,11.02,21.6108,11.3864,24.3288,0.524761905,1.029085714,0.542209524,1.158514286,0.222222222,21,9,4,20,8,0,8,4,0.2,0,0,0,12,5,0,4,0,0,0,0,0,0,0,0,4,0,57.14285714,23.80952381,0,19.04761905,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,36,164,210.9421488,51.49637029,220.2,173.2199962,40,120,138.1818182,53.2889259,208,138.427136,6.25,4.916666667,2.5,0.833333333,3,10,3.16227766,4.158883083,1.44,1.587401052,0.49035617,22,2,0.918558654,12,26.52997535,2.645751311,5.708893809,1,30,3.333333333,0.592592593,3.950617284,12,68,12,1.392147224,4,2.444444444,4,2.444444444,320,672,216,824,8.760253966,48,88,8.844155844,3.791656906,1.909772188,3.726000509,0.544444444,0.4,0.133333333,0,2.583333333,1.21875,9.75,7.269222193,23.25,2.583333333,5.860944251,3.424223667,0.018089019,-0.015638901,0.050982707,0.001465168,8.083333333,3.260991176,1.088438073,9.7766E-05,-0.036065955,0.086062034,1.64376E-05,33,1.392147224,2.197224577,3.610917913,4.394449155,5.198497031,5.993961427,6.803505258,7.601402335,8.412054873,9.210440367,10.02131503,2.833213344,0,4.043051268,0,5.549076085,0,7.136483209,0,8.741456116,2.197224577,2.397895273,2.564949357,1.945910149,0,0,0,0,0,0,2.197224577,2.397895273,2.564949357,1.945910149,0,0,0,0,0,0,10.61186811,3.828641396,3.828641396,1,0,2.858095371,2.353691279,7.328427125,4.242640687,2.914213562,3,1.5,0,0.814269681,0.530330086,0.291421356,0.25,0.25,0,6.132993162,2.449489743,1.316496581,1,0.5,0,0.681443685,0.306186218,0.131649658,0.083333333,0.083333333,0,7.328427125,4.242640687,2.914213562,3,1.5,0,29.69848481,1.81575308,36.08166823,2.282657731,3.675765226,1.860521019,2.087629562,92.23866588,29.6094754,1.409975019,1.954685947,5.038310596,1.392147224,3.134866714,70.36869409,443.3713324,1.392147224,3,3,0.333333333,12.52932501,1.934391668,1.36185921,22.03791425,0.998106356,1.316787107,3.633506249,1.409975019,1.58186266,1.58186266,1.58186266,1.372704688,0.917025179,29.6094754,33.21911587,33.21911587,33.21911587,28.82679845,19.25752876,0.321009363,0.360143065,0.360143065,0.360143065,0.312524018,0.208779351,2.982342404,2.810454762,2.810454762,2.810454762,3.019612735,3.475292244,0.3262375,0.366008556,0.366008556,0.366008556,0.317613958,0.212179647,33.9411255,5.236067977,0.581785331,0.647104749,2.236067977,0.248451997,4.472135955,10.47213595,1.163570662,3.891820298,3.03118777,2.679124626,0.297680514,0.385193625,0.149302498,0.016589166,-0.743339633,7.508764405,0.834307156,0.78801163,88,2.444444444,19.55555556,18.5,0.478085525,0.059760691,3.824684203,5.141663557,20.39230485,2.265811649,1.178519683,20.39230485,2.265811649,25.62837282,40.78460969,4.531623299,11.5881628,20.39230485,6.224558429,8.992308733,12.07368917,15.07242518,18.09180596,0,0,0,0,0,0,0,0,0,88,-9.002843951,1.163570662,0,16,1.777777778,1.083707984,5.302775638,0.589197293,4.920809626,12.09153299,1.343503665,7.661997559,5.536838644,3.970291914,5.342334252,6.84694314,8.428799041,10.05298456,3,0.333333333,0.429409129,3,0.333333333,0.429409129,7.589466384,0.843274043,0.792190118,0.942809042,16.97056275,7.884788477,0.98559856,63.07830782,1.475065102,3.121320344,0.346813372,0.444904509,1,0.111111111,2,6.242640687,0.693626743,2.047692843,2.537634471,1.791759469,0,1.504077397,0,1.32175584,2.914213562,0.323801507,0.418069238,0.085786438,0.009531826,-0.95992323,7.52147884,0.835719871,0.788672914,12.20833333,0.33912037,2.712962963,2.730574348,0.341321793,21.84459478,2.442560881,5.264757106,0.584973012,0.649240506,3.052049914,0.339116657,4.851801288,10.52951421,1.169946024,4.055645836,3.413660393,2.922794379,3.058672431,4.63284684,5.49619853,6.742582799,2.606484411,0.289609379,0.374449619,0.111517353,0.012390817,-0.857391792,7.513587222,0.834843025,0.788262599,88,2.444444444,19.55555556,18.5,0.478085525,0.059760691,3.824684203,5.141663557,20.39230485,2.265811649,1.178519683,20.39230485,2.265811649,25.62837282,40.78460969,4.531623299,11.5881628,20.39230485,6.224558429,8.992308733,12.07368917,15.07242518,18.09180596,0,0,0,0,0,0,0,0,0,36,1,8,36,1,0.125,8,3.610917913,8,0.888888889,0.812780988,8,0.888888889,9,16,1.777777778,7.492760301,8.001321867,4.290459441,6.224558429,8.319961022,10.39699406,12.47668358,0,0,0,0,0,0,0,0,0,67,1.488888889,14.77777778,25.66666667,0.631288311,0.078911039,5.050306488,4.658710953,29.75685071,15.37842535,1.708713928,1.068220682,15.37842535,1.708713928,19.23998085,30.53462848,3.392736498,9.466529359,15.37842622,0.693147181,5.651611602,8.149637841,10.94380981,13.66182289,16.39854589,0,0,0,0,0,0,0,0,0,67.0504,1.490008889,14.78924444,25.64454911,0.630803078,0.078850385,5.046424625,4.660018591,29.78331179,15.3904559,1.710050655,1.068526336,15.3904559,1.710050655,19.25529912,30.5592229,3.395469211,9.472311748,15.39045676,0.69194646,5.653199745,8.151972117,10.94694105,13.66573181,16.40323816,0,0,0,0,0,0,0,0,0,121.516,2.700355556,27.18088889,11.62773124,0.344636918,0.043079615,2.757095341,5.732317219,58.39000457,28.39700229,3.155222476,1.307945247,28.39700229,3.15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compounds,C4H10O2,534-15-6,10795,PubChem,,14.2,90.14,5.63375,8.0636,16.0726,8.716,18.4948,0.503975,1.0045375,0.54475,1.155925,0.333333333,16,6,3,15,5,0,5,2,0.133333333,0,0,0,10,4,0,2,0,0,0,0,0,0,0,0,2,0,62.5,25,0,12.5,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,84,107.4710744,25.97780368,107.22,88.749096,19,51,58.27272727,23.65112406,81.11,59.85696,4.333333333,3.666666667,1.666666667,0.777777778,1,5,2.236067977,2.48490665,1.384615385,1.513085749,0.634373395,14,1,1.119150823,4,13.86815329,2.265686062,3.483798383,1,19,3.166666667,0.555555556,2,7,20,6,1.459147917,4,2.066666667,4,2.066666667,113,217,69,205,5.613960566,29,31,5.922077922,3.125244149,2.922077922,3.084656563,0.511111111,0.280952381,0.047619048,0,1.398148148,1.177083333,4.708333333,3.332801014,14.33333333,2.388888889,4.056390715,2.294121481,0.083864727,0.076254273,-0.242655192,0.020603983,7.888888889,2.175445118,0.662501918,0.002188062,0.191363972,-0.429452374,0.001037193,14,1.459147917,1.791759469,3.044522438,3.663561646,4.317488114,4.96284463,5.624017506,6.274762021,6.938284484,7.590346946,8.254528882,2.397895273,0,3.433987204,0,4.672828834,0,5.96870756,0,7.280008253,1.791759469,1.791759469,1.609437912,0.693147181,0,0,0,0,0,0,1.791759469,1.791759469,1.609437912,0.693147181,0,0,0,0,0,0,8.980172206,3.091042453,3.091042453,1,0,2.457522674,2.031359649,4.991563832,2.808060412,1.921668296,1.39384685,0.288675135,0,0.831927305,0.561612082,0.384333659,0.348461713,0.288675135,0,4.39384685,1.865251371,1.039034086,0.663854611,0.096225045,0,0.732307808,0.373050274,0.207806817,0.165963653,0.096225045,0,4.991563832,2.808060412,1.921668296,1.39384685,0.288675135,0,19.07907262,1.558573039,16.24541588,1.777533865,2.4864189,1.42920136,1.614682216,64,20.78071905,1.298794941,1.825605199,3.760017278,1.918295834,2.55136865,27.38407799,116.5605356,1.459147917,2.446439345,4,0.666666667,11.509775,1.952882292,1.16584986,11.29895826,0.954772093,1.244055344,3.135157771,1.298794941,1.673794941,2.483458593,2.483458593,2.233458593,1.358458593,20.78071905,26.78071905,39.73533749,39.73533749,35.73533749,21.73533749,0.324698735,0.418448735,0.620864648,0.620864648,0.558364648,0.339614648,2.701205059,2.326205059,1.516541407,1.516541407,1.766541407,2.641541407,0.332436988,0.428421247,0.635661156,0.635661156,0.57167165,0.347708378,14.04030206,3.449489743,0.574914957,0.322652915,1.931851653,0.321975275,3.863703305,6.898979486,1.149829914,2.564949357,2.595799778,2.300795291,0.383465882,0.217126788,0.125467544,0.020911257,-0.540881162,4.585015974,0.764169329,0.396804512,31,2.066666667,10.33333333,9.083333333,0.550836957,0.110167391,2.754184786,4.007333185,10.74240473,1.790400788,0.618660906,10.74240473,1.790400788,15.97847271,21.48480946,3.580801576,7.28276118,10.74245678,5.043425117,6.974478911,9.557257866,11.84151673,14.2589257,1.946497979,0.32441633,0.173552374,0,0,0,4.80804425,0.801340708,0.409181074,31,-4.909237255,1.745355992,0,10,1.666666667,0.6,4.302775638,0.717129273,3.920809626,7.841619253,1.306936542,4.912654886,4.609916671,3.433987204,4.672828834,6.008813185,7.396948603,8.814776089,2.449489743,0.40824829,0.233445375,2.449489743,0.40824829,0.233445375,4.629100499,0.77151675,0.399297966,0.936020137,9.459457418,4.97084553,0.994169106,24.85422765,1.174759276,2.115355072,0.352559179,0.195229966,1,0.166666667,2,4.230710143,0.705118357,1.504077397,2.172059387,1.466337069,0,1.268511325,0,1.181499948,2.390833047,0.398472174,0.22712955,0.030476867,0.005079478,-0.909617804,4.587721209,0.764620201,0.396958211,6.756944444,0.450462963,2.252314815,2.067972932,0.413594586,10.33986466,1.954638617,3.370497738,0.561749623,0.316616424,2.434614161,0.405769027,4.056377733,6.740995476,1.123499246,2.764662539,2.819372086,2.462284175,2.310108802,3.802264817,4.305323589,5.43398345,2.25952424,0.376587373,0.212410203,0.09494586,0.01582431,-0.613514381,4.588128707,0.764688118,0.396981355,31,2.066666667,10.33333333,9.083333333,0.550836957,0.110167391,2.754184786,4.007333185,10.74240473,1.790400788,0.618660906,10.74240473,1.790400788,15.97847271,21.48480946,3.580801576,7.28276118,10.74245678,5.043425117,6.974478911,9.557257866,11.84151673,14.2589257,1.946497979,0.32441633,0.173552374,0,0,0,4.80804425,0.801340708,0.409181074,15,1,5,15,1,0.2,5,2.772588722,5,0.833333333,0.419382003,5,0.833333333,6,10,1.666666667,4.875197323,5.018950998,3.433987204,4.795790546,6.447305863,8.045908742,9.657011401,0,0,0,0,0,0,0,0,0,25,1.19047619,8.25,11.9984127,0.689954014,0.137990803,3.449770068,3.622672876,16.61577351,8.557886757,1.42631446,0.559419921,8.557886757,1.42631446,12.41944226,16.94910685,2.824851141,6.146463082,8.558442195,0.405465108,4.570837507,6.316769753,8.635099831,10.71332746,12.89010543,1.930688427,0.321781404,0.171427316,0,0,0,4.813731848,0.802288641,0.409489136,25.0144,1.191161905,8.255,11.98959821,0.689534792,0.137906958,3.447673962,3.623674342,16.62739473,8.563097367,1.427182894,0.559578529,8.563097367,1.427182894,12.4279406,16.95992807,2.826654678,6.149521172,8.5636496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+UMLYSI0441,L,ethyl (E)-2-methylbut-2-enoate,CCOC(=O)/C(=C/C)/C,unsaturated 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amine,C4H11N,75-64-9,6385,PubChem,,21.1,73.16,4.5725,7.6554,15.5198,8.8127,18.5743,0.4784625,0.9699875,0.55079375,1.16089375,0.4,16,5,4,15,4,0,4,0,0,0,0,0,11,4,1,0,0,0,0,0,0,0,0,0,1,0,68.75,25,6.25,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,28,29.31223963,21.35995469,38.6,33.44240009,16,24,24.84507042,16.66611157,34.96,30.40228,4.25,3.583333333,1,0.833333333,3,6,2.449489743,1.386294361,1.176470588,1.319507911,0.8493218,10.5,2,1.341506351,0,9.169934957,1.673320053,2.884053773,1,9,1.8,0.32,0.96,4,12,3,0.721928095,2,1.6,2,1.6,60,82,28,48,3.972356867,12,16,4.961038961,0.977357471,3.961038961,0.969741699,0.6,0,0,0,1.25,1.354166667,2.708333333,4.481741253,11.25,2.25,3.515747535,2.344580192,0.146792719,-0.116049127,0.324982901,0.074489542,7.75,1.827921234,0.597610051,0.006325243,-0.316253916,0.655083456,0.005296794,17,0.721928095,1.609437912,3.044522438,3.496507561,4.394449155,4.859812404,5.771441123,6.240275845,7.155396302,7.625107148,8.541105011,2.197224577,0,3.496507561,0,4.859812404,0,6.240275845,0,7.625107148,1.609437912,1.945910149,0,0,0,0,0,0,0,0,1.609437912,1.945910149,0,0,0,0,0,0,0,0,9.164296433,2.772588722,2.772588722,1,0,2.251291799,1.941646022,4.5,2,3,0,0,0,0.9,0.5,0.5,0,0,0,4.077350269,1.788675135,2.366025404,0,0,0,0.815470054,0.447168784,0.394337567,0,0,0,4.5,2,3,0,0,0,12.25,1.264911064,12.64911064,1.779017701,1.959624428,1.435026478,1.538836749,64,17.94625219,1.121640762,0.970950594,3.25,0.721928095,2.293564443,9.709505945,52,0.721928095,2,3,0.6,6.854752972,1.241146095,1.083984339,8.101474426,1.552537557,1.531034756,0,1.121640762,1.794736718,1.794736718,1.544736718,0.841917187,0,17.94625219,28.71578748,28.71578748,24.71578748,13.47067499,0,0.280410191,0.448684179,0.448684179,0.386184179,0.210479297,0,2.878359238,2.205263282,2.205263282,2.455263282,3.158082813,0,0.287092954,0.459377265,0.459377265,0.395387759,0.21549546,0,8,2,0.4,0.150514998,2,0.4,4,4,0.8,1.791759469,2.444465779,2.121320344,0.424264069,0.163303128,0.707106781,0.141421356,-0.075257499,3.577708764,0.715541753,0.276802492,16,1.6,6.4,7,0.755928946,0.188982237,3.023715784,3.135494216,6.605551275,1.321110255,0.409954534,6.605551275,1.321110255,8.605551275,13.21110255,2.64222051,5.505331536,6.608188235,4.043051268,5.579729826,7.577121931,9.431962767,11.32978357,0,0,0,0,0,0,0,0,0,16,-3.101120416,0.8,0,8,1.6,0.451544993,5,1,4,6.8,1.36,3.891820298,5.066183552,3.36729583,4.859812404,6.444131257,8.048468744,9.656883442,2.236067977,0.447213595,0.174742501,2.236067977,0.447213595,0.174742501,3.651483717,0.730296743,0.281234684,0.8,8,4,1,16,0.916290732,1,0.2,0,1,0.2,2,2,0.4,1.098612289,1.958143765,1.098612289,0,1.098612289,0,1.098612289,2.121320344,0.424264069,0.163303128,0.707106781,0.141421356,-0.075257499,3.577708764,0.715541753,0.276802492,5.5,0.55,2.2,1.511857892,0.377964473,6.047431568,1.781288169,2.409852575,0.481970515,0.190995237,2.409852575,0.481970515,4.069705149,4.819705149,0.96394103,2.33449247,2.685046923,2.277267285,2.339399066,3.745451759,4.243796721,5.383450761,2.046780782,0.409356156,0.155535665,0.507762566,0.101552513,-0.14716966,3.583852385,0.716770477,0.277175057,16,1.6,6.4,7,0.755928946,0.188982237,3.023715784,3.135494216,6.605551275,1.321110255,0.409954534,6.605551275,1.321110255,8.605551275,13.21110255,2.64222051,5.505331536,6.608188235,4.043051268,5.579729826,7.577121931,9.431962767,11.32978357,0,0,0,0,0,0,0,0,0,10,1,4,10,1,0.25,4,2.397895273,4,0.8,0.301029996,4,0.8,5,8,1.6,3.931825633,4.044272764,3.044522438,4.110873864,5.564520407,6.928537818,8.318986125,0,0,0,0,0,0,0,0,0,15.57142857,1.038095238,6.2,7.424908425,0.781959857,0.195489964,3.127839429,3.083593629,12.66410847,6.403482805,1.280696561,0.403208124,6.403482805,1.280696561,8.403482805,12.74982275,2.54996455,5.340085852,6.406821759,0.133531393,3.97375146,5.491284199,7.45160055,9.277051553,11.14325785,0,0,0,0,0,0,0,0,0,15.5716,1.038106667,6.20008,7.424723807,0.781949044,0.195487261,3.127796175,3.083614003,12.66432631,6.403563153,1.280712631,0.403210848,6.403563153,1.280712631,8.403563153,12.75000631,2.550001261,5.340155609,6.406901786,0.133481391,3.973778781,5.491320659,7.451650882,9.277114286,11.14333311,0,0,0,0,0,0,0,0,0,16.9576,1.130506667,6.84688,6.232384673,0.704137384,0.176034346,2.816549534,3.256861992,14.44776271,7.064281355,1.412856271,0.424533994,7.064281355,1.412856271,9.811141375,14.25624271,2.851248542,5.824005159,7.065873904,-0.277025492,4.202520078,5.757489631,7.855742803,9.76270228,11.73479248,1.819539737,0.363907947,0.129980772,0.087221662,0.017444332,-0.529687821,3.911388979,0.782277796,0.296165504,15.5863,1.039086667,6.20694,7.408942501,0.781023033,0.195255758,3.124092131,3.085362308,12.68300905,6.410454526,1.282090905,0.403444412,6.410454526,1.282090905,8.410454526,12.76574905,2.553149811,5.346125554,6.413765763,0.129184458,3.97612295,5.494443975,7.455966481,9.28249143,11.1497849,0,0,0,0,0,0,0,0,0,17.8,1.186666667,7.24,5.678846154,0.664556222,0.166139055,2.658224888,3.36729583,15.54821161,7.474105805,1.494821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amine,C6H15N,13360-63-9,25914,PubChem,,22.1,101.22,4.600909091,10.709,21.287,12.3355,25.4047,0.486772727,0.967590909,0.560704545,1.154759091,0.285714286,22,7,2,21,6,0,6,4,0.19047619,0,0,0,15,6,1,0,0,0,0,0,0,0,0,0,1,0,68.18181818,27.27272727,4.545454545,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22,34,36.33287046,23.38768623,46.3,35.9316,20,28,29.12676056,21.16569525,39.96,29.8808,4.5,4.25,2,1.75,0,5,2.236067977,3.465735903,1.555555556,1.640670712,0.537158271,14.5,0,1.560660172,4,19.24902768,3.055050463,3.627465013,1,33,4.714285714,0.897959184,2.857142857,12,28,9,1.950212065,6,2.666666667,6,2.666666667,202,236,146,186,7.297142506,54,56,6.961038961,5.961038961,5.961038961,5.927860637,0.452380952,0.219387755,0.096598639,0.042857143,0.388888889,0.749166667,2.178888889,6.729604658,12.5,1.785714286,5.312655418,3.520829431,0.131506809,-0.030812547,0.116811031,0.023809524,7.285714286,2.602480803,0.836211705,0.000973526,-0.108345305,0.250205155,0.000333268,13,1.950212065,1.945910149,3.135494216,3.713572067,4.317488114,4.919980926,5.533389489,6.142037406,6.758094504,7.368339686,7.985143931,2.564949357,0,3.496507561,0,4.574710979,0,5.720311777,0,6.900730664,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,8.761236807,3.36729583,3.36729583,1,0,2.627114062,2.132338507,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,0.790790558,0.569035594,0.412132034,0.301776695,0.225592232,0.176776695,5.328427125,3.121320344,1.707106781,0.957106781,0.478553391,0.25,0.761203875,0.520220057,0.341421356,0.239276695,0.159517797,0.125,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,20.98528137,1.840004522,19.78207923,3.097664558,3.327694298,2.602395727,2.952807591,98.10749561,23.99436167,1.090652803,2.398302995,4.168986958,1.950212065,2.774244972,50.3643629,233.4632697,0.863120569,2.751629167,7,1,19.65148445,2.440007983,1.22222776,15.01828953,0.666081376,1.019498601,1.729713704,1.090652803,1.763842123,3.027169118,3.663532755,3.663532755,3.460831318,23.99436167,38.8045267,66.59772061,80.59772061,80.59772061,76.13828899,0.244572155,0.395530703,0.67882398,0.821524595,0.821524595,0.77607005,3.368778815,2.695589496,1.4322625,0.795898864,0.795898864,0.998600301,0.248309195,0.401574375,0.689196346,0.834077413,0.834077413,0.787928327,20.48528137,4.027339492,0.575334213,0.423512769,1.847759065,0.263965581,3.69551813,8.054678984,1.150668426,3.091042453,2.73919265,2.513669746,0.359095678,0.280215753,0.099456184,0.014208026,-0.701657747,5.592530245,0.798932892,0.523325839,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,56,-7.498040439,2.885095623,0,12,1.714285714,0.755426872,3.801937736,0.543133962,3.603875472,8.702204129,1.243172018,5.934894196,4.522970604,3.555348061,4.691347882,5.894402834,7.133295955,8.394121194,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.975489589,10.82842712,5.985143843,0.997523974,35.91086306,1.310251729,2.366025404,0.338003629,0.261813582,1,0.142857143,2,4.732050808,0.676007258,1.681758574,2.316436311,1.609437912,0,1.446918983,0,1.354545663,2.618591722,0.374084532,0.292647453,0.169101979,0.024157426,-0.540295917,5.594918316,0.799274045,0.523455625,7.989722222,0.380462963,2.282777778,2.564019422,0.42733657,15.38411653,2.102137974,4.119483043,0.588497578,0.430389904,2.400399559,0.342914223,3.963351092,8.238966087,1.176995155,3.346584212,2.935808684,2.621413019,2.328341703,3.846629402,4.257734494,5.383121673,2.490590677,0.355798668,0.277411651,0.067967869,0.009709696,-0.817387444,5.593895954,0.799127993,0.523400069,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,53,1.892857143,15.12244898,11.95358555,0.432700019,0.07211667,2.596200114,4.744044423,31.33744926,15.7401532,2.248593314,0.837906269,15.7401532,2.248593314,25.20200528,31.43949008,4.491355725,8.651145924,15.74015364,0.133531393,5.859564476,8.013356692,11.14898088,13.69900254,16.58345192,2.370603005,0.338657572,0.262401181,0.035061232,0.005008747,-1.018620979,5.716223251,0.816603322,0.529976427,53.0012,1.8929,15.1228,11.95322862,0.432689477,0.072114913,2.59613686,4.744085169,31.3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From 16b711b530a68179fe4235cae7357d5179196632 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Sun, 27 Oct 2019 20:44:00 -0400 Subject: [PATCH 06/40] Force UTF-8 encoding for databases --- ecnet/utils/data_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ecnet/utils/data_utils.py b/ecnet/utils/data_utils.py index bbdee25..3f8a871 100644 --- a/ecnet/utils/data_utils.py +++ b/ecnet/utils/data_utils.py @@ -58,7 +58,7 @@ def __init__(self, filename: str): if '.csv' not in filename: filename += '.csv' try: - with open(filename, newline='') as file: + with open(filename, newline='', encoding='utf8') as file: rows = list(reader(file)) except FileNotFoundError: raise Exception('CSV database not found: {}'.format(filename)) From 5531ab0f878b28c4c2c066b10da23c9c44c40b72 Mon Sep 17 00:00:00 2001 From: Kessler Date: Wed, 13 Nov 2019 12:49:39 -0500 Subject: [PATCH 07/40] Update to default learning config variables --- ecnet/utils/server_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ecnet/utils/server_utils.py b/ecnet/utils/server_utils.py index 12ec84f..f5312cc 100644 --- a/ecnet/utils/server_utils.py +++ b/ecnet/utils/server_utils.py @@ -65,10 +65,10 @@ def default_config() -> dict: return { 'epochs': 3000, - 'learning_rate': 0.001, + 'learning_rate': 0.01, 'beta_1': 0.9, 'beta_2': 0.999, - 'epsilon': 0.0000001, + 'epsilon': 1e-8, 'decay': 0.0, 'hidden_layers': [ [32, 'relu'], From 5a8e8a44c3a4bf51dd93f4c1cb3372ae5c46d0e8 Mon Sep 17 00:00:00 2001 From: Kessler Date: Wed, 13 Nov 2019 12:50:06 -0500 Subject: [PATCH 08/40] Addition of argument for HP tuning --- ecnet/server.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/ecnet/server.py b/ecnet/server.py index 226ae55..1ff89a8 100644 --- a/ecnet/server.py +++ b/ecnet/server.py @@ -146,7 +146,7 @@ def limit_inputs(self, limit_num: int, num_estimators: int=None, def tune_hyperparameters(self, num_employers: int, num_iterations: int, shuffle: bool=None, split: list=None, validate: bool=True, eval_set: str=None, - eval_fn: str='rmse'): + eval_fn: str='rmse', epochs: int=300): '''Tunes neural network learning hyperparameters using an artificial bee colony algorithm; tuned hyperparameters are saved to Server's model configuration file @@ -162,6 +162,7 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int, `train`, `test`, None (all sets) eval_fn (str): error function used to evaluate bee fitness; `rmse`, `mean_abs_error`, `med_abs_error` + epochs (int): number of training epochs per bee ANN (def: 300) ''' self._vars = tune_hyperparameters( @@ -174,7 +175,8 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int, split, validate, eval_set, - eval_fn + eval_fn, + epochs ) save_config(self._vars, self._cf_file) From d04c3b59a322da2b3077b22a1b61c1c520c0514a Mon Sep 17 00:00:00 2001 From: Kessler Date: Wed, 13 Nov 2019 12:50:38 -0500 Subject: [PATCH 09/40] HP tuning now only affects LR, LR decay, hidden layer size; add epoch arg --- ecnet/tasks/tuning.py | 48 +++++++++++++++---------------------------- 1 file changed, 17 insertions(+), 31 deletions(-) diff --git a/ecnet/tasks/tuning.py b/ecnet/tasks/tuning.py index dbc41ee..c8681dd 100644 --- a/ecnet/tasks/tuning.py +++ b/ecnet/tasks/tuning.py @@ -25,7 +25,7 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int, num_iterations: int, num_processes: int=1, shuffle: str=None, split: list=None, validate: bool=True, eval_set: str=None, - eval_fn: str='rmse') -> dict: + eval_fn: str='rmse', epochs: int=300) -> dict: '''Tunes neural network learning/architecture hyperparameters Args: @@ -41,6 +41,7 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int, `train`, `test`, None (all sets) eval_fn (str): error function used to evaluate bee performance; `rmse`, `mean_abs_error`, `med_abs_error` + epochs (int): number of training epochs per bee ANN (def: 300) Returns: dict: tuned hyperparameters @@ -67,20 +68,17 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int, 'validate': validate, 'eval_set': eval_set, 'eval_fn': eval_fn, - 'hidden_layers': vars['hidden_layers'] + 'hidden_layers': vars['hidden_layers'], + 'epochs': epochs } value_ranges = [ - ('float', (0.0, 1.0)), - ('float', (0.0, 1.0)), - ('float', (0.0, 1.0)), - ('float', (0.0, 1.0)), - ('float', (0.0, 1.0)), - ('int', (1, len(df.learn_set))) + ('float', (1e-9, 1e-4)), + ('float', (1e-5, 0.1)) ] for _ in range(len(vars['hidden_layers'])): - value_ranges.append(('int', (1, 100))) + value_ranges.append(('int', (1, len(df._input_names)))) abc = ABC( tune_fitness_function, @@ -101,24 +99,16 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int, abc.run_iteration() logger.log('info', 'Best Performer: {}, {}'.format( abc.best_performer[2], { - 'beta_1': abc.best_performer[1][0], - 'beta_2': abc.best_performer[1][1], - 'decay': abc.best_performer[1][2], - 'epsilon': abc.best_performer[1][3], - 'learning_rate': abc.best_performer[1][4], - 'batch_size': abc.best_performer[1][5], - 'hidden_layers': abc.best_performer[1][6:] + 'decay': abc.best_performer[1][0], + 'learning_rate': abc.best_performer[1][1], + 'hidden_layers': abc.best_performer[1][2:] } ), call_loc='TUNE') params = abc.best_performer[1] - vars['beta_1'] = params[0] - vars['beta_2'] = params[1] - vars['decay'] = params[2] - vars['epsilon'] = params[3] - vars['learning_rate'] = params[4] - vars['batch_size'] = params[5] + vars['decay'] = params[0] + vars['learning_rate'] = params[1] for l_idx in range(len(vars['hidden_layers'])): - vars['hidden_layers'][l_idx][0] = params[6 + l_idx] + vars['hidden_layers'][l_idx][0] = params[2 + l_idx] return vars @@ -134,16 +124,12 @@ def tune_fitness_function(params: dict, **kwargs): ''' vars = default_config() - vars['beta_1'] = params[0] - vars['beta_2'] = params[1] - vars['decay'] = params[2] - vars['epsilon'] = params[3] - vars['learning_rate'] = params[4] - vars['batch_size'] = params[5] + vars['decay'] = params[0] + vars['learning_rate'] = params[1] vars['hidden_layers'] = kwargs['hidden_layers'] - vars['epochs'] = 3000 + vars['epochs'] = kwargs['epochs'] for l_idx in range(len(vars['hidden_layers'])): - vars['hidden_layers'][l_idx][0] = params[6 + l_idx] + vars['hidden_layers'][l_idx][0] = params[2 + l_idx] df = kwargs['df'] if kwargs['shuffle'] is not None: From e98b2a8001ddfe1e3dc54ec71aaae9f97b7c8d3f Mon Sep 17 00:00:00 2001 From: Kessler Date: Wed, 13 Nov 2019 13:41:20 -0500 Subject: [PATCH 10/40] Added option to select inputs based on set-specific RFR --- ecnet/server.py | 9 +++++++-- ecnet/tasks/limit_inputs.py | 17 +++++++---------- 2 files changed, 14 insertions(+), 12 deletions(-) diff --git a/ecnet/server.py b/ecnet/server.py index 1ff89a8..2988df0 100644 --- a/ecnet/server.py +++ b/ecnet/server.py @@ -112,7 +112,8 @@ def create_project(self, project_name: str, num_pools: int=1, num_candidates), call_loc='PROJECT') def limit_inputs(self, limit_num: int, num_estimators: int=None, - output_filename: str=None, **kwargs) -> list: + eval_set: str='learn', output_filename: str=None, + **kwargs) -> list: '''Selects `limit_num` influential input parameters using random forest regression @@ -122,6 +123,8 @@ def limit_inputs(self, limit_num: int, num_estimators: int=None, defaults to the total number of inputs output_filename (str): if not None, new limited database is saved here + eval_set (str): set to perform RFR on (`learn`, `valid`, `train`, + `test`, None (all)) (default: `learn`) **kwargs: any argument accepted by sklearn.ensemble.RandomForestRegressor @@ -133,7 +136,9 @@ def limit_inputs(self, limit_num: int, num_estimators: int=None, self._df, limit_num, num_estimators, - self._num_processes + self._num_processes, + eval_set, + **kwargs ) self._df.set_inputs([r[0] for r in result]) self._sets = self._df.package_sets() diff --git a/ecnet/tasks/limit_inputs.py b/ecnet/tasks/limit_inputs.py index 3902730..58fa8ae 100644 --- a/ecnet/tasks/limit_inputs.py +++ b/ecnet/tasks/limit_inputs.py @@ -18,10 +18,12 @@ # ECNet imports from ecnet.utils.data_utils import DataFrame from ecnet.utils.logging import logger +from ecnet.utils.server_utils import get_x, get_y def limit_rforest(df: DataFrame, limit_num: int, num_estimators: int=None, - num_processes: int=1, **kwargs) -> list: + num_processes: int=1, eval_set: str='learn', + **kwargs) -> list: '''Uses random forest regression to select input parameters Args: @@ -29,6 +31,8 @@ def limit_rforest(df: DataFrame, limit_num: int, num_estimators: int=None, limit_num (int): desired number of input parameters num_estimators (int): number of trees used by RFR algorithm num_processes (int): number of parallel jobs for RFR algorithm + eval_set (str): set to perform RFR on (`learn`, `valid`, `train`, + `test`, None (all)) (default: `learn`) **kwargs: any argument accepted by sklearn.ensemble.RandomForestRegressor @@ -40,15 +44,8 @@ def limit_rforest(df: DataFrame, limit_num: int, num_estimators: int=None, .format(limit_num), call_loc='LIMIT') pd = df.package_sets() - X = pd.learn_x - y = pd.learn_y - if len(pd.valid_x) > 0: - X = concatenate((X, pd.valid_x)) - y = concatenate((y, pd.valid_y)) - if len(pd.test_x) > 0: - X = concatenate((X, pd.test_x)) - y = concatenate((y, pd.test_y)) - y = ravel(y) + X = get_x(pd, eval_set) + y = ravel(get_y(pd, eval_set)) if num_estimators is None: num_estimators = len(X[0]) From f63bb4fd74087d8620c571018082d5837d44d88f Mon Sep 17 00:00:00 2001 From: Kessler Date: Wed, 13 Nov 2019 13:41:42 -0500 Subject: [PATCH 11/40] Update unit test for set-specific selection --- tests/tasks/test_limit_inputs.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/tests/tasks/test_limit_inputs.py b/tests/tasks/test_limit_inputs.py index 1ef6862..dc4d319 100644 --- a/tests/tasks/test_limit_inputs.py +++ b/tests/tasks/test_limit_inputs.py @@ -23,6 +23,38 @@ def test_limit_rforest(self): df.set_inputs([r[0] for r in result]) self.assertEqual(len(df._input_names), 2) + def test_limit_sets(self): + + print('\nUNIT TEST: limit_sets') + df = DataFrame(DB_LOC) + df.create_sets() + result = limit_rforest(df, 2, eval_set='valid') + self.assertEqual(len(result), 2) + self.assertEqual(len(result[0]), 2) + df.set_inputs([r[0] for r in result]) + self.assertEqual(len(df._input_names), 2) + df = DataFrame(DB_LOC) + df.create_sets() + result = limit_rforest(df, 2, eval_set='train') + self.assertEqual(len(result), 2) + self.assertEqual(len(result[0]), 2) + df.set_inputs([r[0] for r in result]) + self.assertEqual(len(df._input_names), 2) + df = DataFrame(DB_LOC) + df.create_sets() + result = limit_rforest(df, 2, eval_set='test') + self.assertEqual(len(result), 2) + self.assertEqual(len(result[0]), 2) + df.set_inputs([r[0] for r in result]) + self.assertEqual(len(df._input_names), 2) + df = DataFrame(DB_LOC) + df.create_sets() + result = limit_rforest(df, 2, eval_set=None) + self.assertEqual(len(result), 2) + self.assertEqual(len(result[0]), 2) + df.set_inputs([r[0] for r in result]) + self.assertEqual(len(df._input_names), 2) + def test_server_limit(self): print('\nUNIT TEST: limit_rforest (Server)') From d59bad46af54ff22ce903a7d447c2bc359a49d30 Mon Sep 17 00:00:00 2001 From: Kessler Date: Wed, 13 Nov 2019 20:04:24 -0500 Subject: [PATCH 12/40] Initial upload of workflows module --- ecnet/workflows/ecrl_workflow.py | 166 ++++++++++++++++++++++++++++++ ecnet/workflows/workflow_utils.py | 108 +++++++++++++++++++ 2 files changed, 274 insertions(+) create mode 100644 ecnet/workflows/ecrl_workflow.py create mode 100644 ecnet/workflows/workflow_utils.py diff --git a/ecnet/workflows/ecrl_workflow.py b/ecnet/workflows/ecrl_workflow.py new file mode 100644 index 0000000..dfc7c86 --- /dev/null +++ b/ecnet/workflows/ecrl_workflow.py @@ -0,0 +1,166 @@ +from ecnet.tasks.tuning import tune_hyperparameters +from ecnet.tools.database import create_db +from ecnet.tools.plotting import ParityPlot +from ecnet.utils.data_utils import DataFrame +from ecnet.utils.error_utils import calc_med_abs_error, calc_r2 +from ecnet.utils.logging import logger +from ecnet.utils.server_utils import default_config, save_config, train_model,\ + use_model +from workflow_utils import find_optimal_num_inputs, prop_range_from_split +# from ecnet.workflows.workflow_utils import find_optimal_num_inputs,\ +# prop_range_from_split + +from datetime import datetime + +from matplotlib import pyplot as plt + + +def create_model(prop_abvr: str, smiles: list=None, targets: list=None, + db_name: str=None, qspr_backend: str='padel', + create_plots: bool=True, data_split: list=[0.7, 0.2, 0.1], + log_level: str='info', log_to_file: bool=True, + num_processes: int=1): + ''' create_model: ECRL's database/model creation workflow for all + publications + + Args: + prop_abvr (str): abbreviation for the property name (e.g. CN) + smiles (list): if supplied with targets, creates a new database + targets (list): if supplied with smiles, creates a new database + db_name (str): you may supply an existing ECNet-formatted database + qspr_backend (str): if creating new database, generation software to + use (`padel`, `alvadesc`) + create_plots (bool): if True, creates plots for median absolute error + vs. number of descriptors as inputs, parity plot for all sets + data_split (list): [learn %, valid %, test %] for all supplied data + log_level (str): `debug`, `info`, `warn`, `error`, `crit` + log_to_file (bool): if True, saves workflow logs to a file in `logs` + directory + num_processes (int): number of concurrent processes to use for various + tasks + ''' + + # Initialize logging + logger.stream_level = log_level + if log_to_file: + logger.file_level = log_level + + # If database not supplied, create database from supplied SMILES, targets + if db_name is None: + if smiles is None or targets is None: + raise ValueError('Must supply SMILES and target values') + db_name = datetime.now().strftime('{}_model_%Y%m%d.csv'.format( + prop_abvr + )) + logger.log('info', 'Creating database {}...'.format(db_name), + 'WORKFLOW') + create_db(smiles, db_name, targets, prop_abvr, backend=qspr_backend) + logger.log('info', 'Created database {}'.format(db_name), 'WORKFLOW') + + # Create database split, each subset has proportionally equal number of + # compounds based on range of experimental/target values + logger.log('info', 'Creating optimal data split...', 'WORKFLOW') + prop_range_from_split(db_name, data_split) + logger.log('info', 'Created optimal data split', 'WORKFLOW') + df = DataFrame(db_name) + df.create_sets() + logger.log('info', '\tLearning set: {}'.format(len(df.learn_set)), + 'WORKFLOW') + logger.log('info', '\tValidation set: {}'.format(len(df.valid_set)), + 'WORKFLOW') + logger.log('info', '\tTest set: {}'.format(len(df.test_set)), 'WORKFLOW') + + # Find optimal number of QSPR input variables + logger.log('info', 'Finding optimal number of inputs...', 'WORKFLOW') + errors, desc = find_optimal_num_inputs(db_name, 'valid', num_processes) + df = DataFrame(db_name) + df.set_inputs(desc) + df.save(db_name) + logger.log('info', 'Found optimal number of inputs', 'WORKFLOW') + logger.log('info', '\tNumber of inputs: {}'.format(len(df._input_names)), + 'WORKFLOW') + + # Plot the curve of MAE vs. num. desc. added, if desired + if create_plots: + logger.log('info', 'Creating plot of MAE vs. descriptors...', + 'WORKFLOW') + num_add = [e[0] for e in errors] + maes = [e[1] for e in errors] + opt_num = len(desc) + plt.clf() + plt.rcParams['font.family'] = 'Times New Roman' + plt.plot(num_add, maes, c='blue') + plt.axvline(x=opt_num, c='red', linestyle='--') + plt.xlabel('Number of Descriptors as ANN Input Variables') + plt.ylabel('Median Absolute Error of {} Predictions'.format(prop_abvr)) + plt.savefig(db_name.replace('.csv', '_desc_curve.png')) + logger.log('info', 'Created plot of MAE vs. descriptors', 'WORKFLOW') + + # Tune ANN hyperparameters according to validation set performance + logger.log('info', 'Tuning ANN hyperparameters...', 'WORKFLOW') + config = default_config() + config = tune_hyperparameters(df, config, 25, 10, num_processes, + eval_set='valid', eval_fn='med_abs_error', + epochs=300, validate=False) + config['epochs'] = default_config()['epochs'] + config_filename = db_name.replace('.csv', '.yml') + save_config(config, config_filename) + logger.log('info', 'Tuned ANN hyperparameters', 'WORKFLOW') + logger.log('info', '\tLearning rate: {}'.format(config['learning_rate']), + 'WORKFLOW') + logger.log('info', '\tLR decay: {}'.format(config['decay']), 'WORKFLOW') + logger.log('info', '\tHidden layers: {}'.format(config['hidden_layers']), + 'WORKFLOW') + + # Create Model + logger.log('info', 'Generating ANN...', 'WORKFLOW') + df.create_sets() + sets = df.package_sets() + _ = train_model(sets, config, None, 'rmse', False, + db_name.replace('.csv', '.h5'), True, True) + logger.log('info', 'Generated ANN', 'WORKFLOW') + + logger.log('info', 'Measuring ANN performance...', 'WORKFLOW') + preds_learn = use_model(sets, 'learn', db_name.replace('.csv', '.h5')) + preds_valid = use_model(sets, 'valid', db_name.replace('.csv', '.h5')) + preds_test = use_model(sets, 'test', db_name.replace('.csv', '.h5')) + learn_mae = calc_med_abs_error(preds_learn, sets.learn_y) + learn_r2 = calc_r2(preds_learn, sets.learn_y) + valid_mae = calc_med_abs_error(preds_valid, sets.valid_y) + valid_r2 = calc_r2(preds_valid, sets.valid_y) + test_mae = calc_med_abs_error(preds_test, sets.test_y) + test_r2 = calc_r2(preds_test, sets.test_y) + logger.log('info', 'Measured ANN performance', 'WORKFLOW') + logger.log('info', '\tLearning set:\t R2: {}\t MAE: {}'.format( + learn_r2, learn_mae), 'WORKFLOW') + logger.log('info', '\tValidation set:\t R2: {}\t MAE: {}'.format( + valid_r2, valid_mae), 'WORKFLOW') + logger.log('info', '\tTesting set:\t R2: {}\t MAE: {}'.format( + test_r2, test_mae), 'WORKFLOW') + + if create_plots: + logger.log('info', 'Creating parity plot...', 'WORKFLOW') + plt.clf() + parity_plot = ParityPlot( + '{} Parity Plot'.format(prop_abvr), + 'Experimental {} Value'.format(prop_abvr), + 'Predicted {} Value'.format(prop_abvr) + ) + parity_plot.add_series(sets.learn_y, preds_learn, 'Learning Set', + 'blue') + parity_plot.add_series(sets.valid_y, preds_valid, 'Validation Set', + 'green') + parity_plot.add_series(sets.test_y, preds_test, 'Test Set', 'red') + parity_plot.add_error_bars(test_mae, 'Test MAE') + parity_plot._add_label('Test $R^2$', test_r2) + parity_plot._add_label('Validation MAE', valid_mae) + parity_plot._add_label('Validation $R^2$', valid_r2) + parity_plot._add_label('Learning MAE', learn_mae) + parity_plot._add_label('Learning $R^2$', learn_r2) + parity_plot.save(db_name.replace('.csv', '_parity.png')) + logger.log('info', 'Created parity plot', 'WORKFLOW') + + +if __name__ == '__main__': + + create_model('CN', db_name='cn_database_v1.0.csv', num_processes=4) diff --git a/ecnet/workflows/workflow_utils.py b/ecnet/workflows/workflow_utils.py new file mode 100644 index 0000000..f9ab635 --- /dev/null +++ b/ecnet/workflows/workflow_utils.py @@ -0,0 +1,108 @@ +from ecnet.utils.data_utils import DataFrame +from ecnet.utils.server_utils import default_config, train_model +from ecnet.tasks.limit_inputs import limit_rforest + +from multiprocessing import Pool, set_start_method +from os import name + + +def prop_range_from_split(db_name: str, data_split: list): + ''' prop_range_from_split: creates learning, validation, test subsets, each + with proportionally equal number of compounds based on experimental value + range + + Args: + db_name (str): name/location of ECNet-formatted database + data_split (list): [learn %, valid %, test %] for all supplied data + ''' + + df = DataFrame(db_name) + for mol in df.data_points: + mol.assignment = 'L' + mol.TARGET = float(mol.TARGET) + df.data_points = sorted(df.data_points, key=lambda m: m.TARGET, + reverse=True) + num_validate = int(len(df) / (data_split[1] * len(df.data_points))) + num_test = int(len(df) / (data_split[2] * len(df.data_points))) + for idx, mol in enumerate(df.data_points): + if (idx + 1) % num_validate == 0: + mol.assignment = 'V' + if (idx + 3) % num_test == 0: + mol.assignment = 'T' + if len([m for m in df.data_points if m.assignment == 'V']) == 0: + raise ValueError('Data split resulted in empty validation set') + if len([m for m in df.data_points if m.assignment == 'T']) == 0: + raise ValueError('Data split resulted in empty test set') + df.data_points = sorted(df.data_points, key=lambda m: m.id) + df.save(db_name) + + +def find_optimal_num_inputs(db_name: str, eval_set: str, + num_processes: int) -> tuple: + ''' find_optimal_num_inputs: find the optimal number of input variables, + return names of variables; optimal number of variables produces lowest + median absolute error; variables added 25 at a time, according to RFR + importance score (most-to-least important) + + Args: + db_name (str): name/location of ECNet-formatted database + eval_set (str): set to evaluate (`learn`, `valid`, `train`, `test`, + None (all)) + num_processes (int): number of concurrent processes to run for RFR, + training + + Returns: + tuple: ([addition1, error1, ..., additionN, errorN], opt_desc) + ''' + + conf = default_config() + conf['epochs'] = 300 + df = DataFrame(db_name) + df.create_sets() + desc = limit_rforest(df, len(df._input_names), num_processes=num_processes, + eval_set=eval_set) + desc = [d[0] for d in desc] + + errors = [] + if num_processes > 1: + if name != 'nt': + set_start_method('spawn', force=True) + train_pool = Pool(processes=num_processes) + + for d_idx in range(0, len(desc), 10): + if d_idx >= len(desc) - 1: + to_use = desc[:] + else: + to_use = desc[: d_idx + 1] + df = DataFrame(db_name) + df.set_inputs(to_use) + df.create_sets() + sets = df.package_sets() + + if num_processes > 1: + errors.append([d_idx, train_pool.apply_async( + train_model, [ + sets, conf, eval_set, 'med_abs_error', False, '_.h5', + False, False + ] + )]) + else: + errors.append([d_idx, train_model( + sets, conf, eval_set, 'med_abs_error', False, '_.h5', + False, False + )]) + + if num_processes > 1: + train_pool.close() + train_pool.join() + for idx, err in enumerate(errors): + errors[idx][1] = err[1].get() + + min_error = errors[0][1] + opt_num_desc = 1 + for err in errors[1:]: + if err[1] < min_error: + min_error = err[1] + opt_num_desc = err[0] + + return (errors, desc[: opt_num_desc]) From bf39180634835a67bb88feea9f13361fa08e366e Mon Sep 17 00:00:00 2001 From: tjkessler Date: Thu, 14 Nov 2019 18:57:44 -0500 Subject: [PATCH 13/40] Baked workflows into ECNet package --- ecnet/workflows/__init__.py | 2 ++ ecnet/workflows/ecrl_workflow.py | 10 ++-------- 2 files changed, 4 insertions(+), 8 deletions(-) create mode 100644 ecnet/workflows/__init__.py diff --git a/ecnet/workflows/__init__.py b/ecnet/workflows/__init__.py new file mode 100644 index 0000000..52dfc8c --- /dev/null +++ b/ecnet/workflows/__init__.py @@ -0,0 +1,2 @@ +import ecnet.workflows.ecrl_workflow +import ecnet.workflows.workflow_utils diff --git a/ecnet/workflows/ecrl_workflow.py b/ecnet/workflows/ecrl_workflow.py index dfc7c86..3096f46 100644 --- a/ecnet/workflows/ecrl_workflow.py +++ b/ecnet/workflows/ecrl_workflow.py @@ -6,9 +6,8 @@ from ecnet.utils.logging import logger from ecnet.utils.server_utils import default_config, save_config, train_model,\ use_model -from workflow_utils import find_optimal_num_inputs, prop_range_from_split -# from ecnet.workflows.workflow_utils import find_optimal_num_inputs,\ -# prop_range_from_split +from ecnet.workflows.workflow_utils import find_optimal_num_inputs,\ + prop_range_from_split from datetime import datetime @@ -159,8 +158,3 @@ def create_model(prop_abvr: str, smiles: list=None, targets: list=None, parity_plot._add_label('Learning $R^2$', learn_r2) parity_plot.save(db_name.replace('.csv', '_parity.png')) logger.log('info', 'Created parity plot', 'WORKFLOW') - - -if __name__ == '__main__': - - create_model('CN', db_name='cn_database_v1.0.csv', num_processes=4) From 5c985817720b8c8231afdf7812e0c893a2fc71a5 Mon Sep 17 00:00:00 2001 From: Kessler Date: Sun, 17 Nov 2019 16:07:07 -0500 Subject: [PATCH 14/40] Added option to change model filename if not using project --- ecnet/server.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/ecnet/server.py b/ecnet/server.py index 2988df0..d69da76 100644 --- a/ecnet/server.py +++ b/ecnet/server.py @@ -187,7 +187,7 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int, def train(self, shuffle: str=None, split: list=None, retrain: bool=False, validate: bool=False, selection_set: str=None, - selection_fn: str='rmse'): + selection_fn: str='rmse', filename: str='model.h5'): '''Trains neural network(s) using currently-loaded data; single NN if no project is created, all candidates if created @@ -203,6 +203,7 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, set; `learn`, `valid`, `train`, `test`, None (all data) selection_fn (str): candidates are selected based on this error metric; `rmse`, `mean_abs_error`, `med_abs_error` + filename (str): if project not created, saves Keras h5 model here ''' if self._prj_name is None: @@ -213,7 +214,8 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, selection_set, selection_fn, retrain, - validate=validate + filename, + validate ) else: From 00f465ae5f6070c793ddff887c67b3a5a37df78e Mon Sep 17 00:00:00 2001 From: Travis Kessler Date: Wed, 25 Dec 2019 12:28:04 -0500 Subject: [PATCH 15/40] Additional methods have arg for h5 filename --- ecnet/server.py | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/ecnet/server.py b/ecnet/server.py index d69da76..a93656c 100644 --- a/ecnet/server.py +++ b/ecnet/server.py @@ -187,7 +187,7 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int, def train(self, shuffle: str=None, split: list=None, retrain: bool=False, validate: bool=False, selection_set: str=None, - selection_fn: str='rmse', filename: str='model.h5'): + selection_fn: str='rmse', model_filename: str='model.h5'): '''Trains neural network(s) using currently-loaded data; single NN if no project is created, all candidates if created @@ -203,7 +203,8 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, set; `learn`, `valid`, `train`, `test`, None (all data) selection_fn (str): candidates are selected based on this error metric; `rmse`, `mean_abs_error`, `med_abs_error` - filename (str): if project not created, saves Keras h5 model here + model_filename (str): if project not created, saves Keras h5 model + here ''' if self._prj_name is None: @@ -214,7 +215,7 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, selection_set, selection_fn, retrain, - filename, + model_filename, validate ) @@ -235,7 +236,8 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, self._num_processes ) - def use(self, dset: str=None, output_filename: str=None) -> list: + def use(self, dset: str=None, output_filename: str=None, + model_filename: str='model.h5') -> list: '''Uses trained neural network(s) to predict for specified set; single NN if no project created, best pool candidates if created @@ -243,13 +245,14 @@ def use(self, dset: str=None, output_filename: str=None) -> list: dset (str): set to predict for; `learn`, `valid`, `train`, `test`, None (all sets) output_filename (str): if supplied, saves results to this CSV file + model_filename (str): if supplied, use specified .h5 model file Returns: list: list of results for specified set ''' if self._prj_name is None: - results = use_model(self._sets, dset) + results = use_model(self._sets, dset, model_filename) else: results = use_project( @@ -264,7 +267,8 @@ def use(self, dset: str=None, output_filename: str=None) -> list: call_loc='USE') return results - def errors(self, *args, dset: str=None) -> dict: + def errors(self, *args, dset: str=None, + model_filename: str='model.h5') -> dict: '''Obtains various errors for specified set Args: @@ -272,6 +276,8 @@ def errors(self, *args, dset: str=None) -> dict: `med_abs_error`, `r2` dset (str): set to obtain errors for; `learn`, `valid`, `train`, `test`, None (all sets) + model_filename (str): if specified, uses .h5 model file for error + calculations Returns: dict: {'error_fn', value ...} with supplied errors @@ -280,7 +286,7 @@ def errors(self, *args, dset: str=None) -> dict: for err in args: logger.log('debug', 'Calculating {} for {} set'.format(err, dset), call_loc='ERRORS') - preds = self.use(dset) + preds = self.use(dset, model_filename=model_filename) y_vals = get_y(self._sets, dset) errors = {} for err in args: From c9a0190dac5e91633b57d54771a9d20b1c8442e4 Mon Sep 17 00:00:00 2001 From: Travis Kessler Date: Wed, 25 Dec 2019 16:55:18 -0500 Subject: [PATCH 16/40] Updated unit tests with default hyperparam vals --- tests/utils/test_server_utils.py | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/tests/utils/test_server_utils.py b/tests/utils/test_server_utils.py index 452c423..e83c8f3 100644 --- a/tests/utils/test_server_utils.py +++ b/tests/utils/test_server_utils.py @@ -30,10 +30,10 @@ def test_default_config(self): self.assertEqual( dc, { 'epochs': 3000, - 'learning_rate': 0.001, + 'learning_rate': 0.01, 'beta_1': 0.9, 'beta_2': 0.999, - 'epsilon': 0.0000001, + 'epsilon': 1e-8, 'decay': 0.0, 'hidden_layers': [ [32, 'relu'], @@ -56,12 +56,12 @@ def test_get_candidate_path(self): ) self.assertEqual( server_utils.get_candidate_path(prj_name, pool, candidate, True), - join('test_project', 'pool_1', 'candidate_2', 'model.h5') + join('test_project', 'pool_1', 'candidate_2', 'model.ecnet') ) self.assertEqual( server_utils.get_candidate_path(prj_name, pool, candidate, p_best=True), - join('test_project', 'pool_1', 'model.h5') + join('test_project', 'pool_1', 'model.ecnet') ) def test_get_error(self): @@ -126,10 +126,10 @@ def test_open_save_config(self): server_utils.open_config('config.yml'), { 'epochs': 3000, - 'learning_rate': 0.001, + 'learning_rate': 0.01, 'beta_1': 0.9, 'beta_2': 0.999, - 'epsilon': 0.0000001, + 'epsilon': 1e-8, 'decay': 0.0, 'hidden_layers': [ [32, 'relu'], @@ -163,10 +163,10 @@ def test_train_model(self): config = server_utils.default_config() config['epochs'] = 100 r_squared = server_utils.train_model( - pd, config, 'test', 'r2', filename='test_train.h5' + pd, config, 'test', 'r2', filename='test_train.ecnet' ) - self.assertTrue(exists('test_train.h5')) - remove('test_train.h5') + self.assertTrue(exists('test_train.ecnet')) + remove('test_train.ecnet') def test_use_model(self): @@ -177,29 +177,29 @@ def test_use_model(self): config = server_utils.default_config() config['epochs'] = 100 _ = server_utils.train_model( - pd, config, 'test', 'rmse', filename='test_use.h5' + pd, config, 'test', 'rmse', filename='test_use.ecnet' ) self.assertEqual( - len(server_utils.use_model(pd, 'learn', 'test_use.h5')), + len(server_utils.use_model(pd, 'learn', 'test_use.ecnet')), len(pd.learn_y) ) self.assertEqual( - len(server_utils.use_model(pd, 'valid', 'test_use.h5')), + len(server_utils.use_model(pd, 'valid', 'test_use.ecnet')), len(pd.valid_y) ) self.assertEqual( - len(server_utils.use_model(pd, 'test', 'test_use.h5')), + len(server_utils.use_model(pd, 'test', 'test_use.ecnet')), len(pd.test_y) ) self.assertEqual( - len(server_utils.use_model(pd, 'train', 'test_use.h5')), + len(server_utils.use_model(pd, 'train', 'test_use.ecnet')), len(pd.learn_y) + len(pd.valid_y) ) self.assertEqual( - len(server_utils.use_model(pd, None, 'test_use.h5')), + len(server_utils.use_model(pd, None, 'test_use.ecnet')), len(pd.learn_y) + len(pd.valid_y) + len(pd.test_y) ) - remove('test_use.h5') + remove('test_use.ecnet') if __name__ == '__main__': From f9af76ff83b839050a43c579fff0c8f309b28dc9 Mon Sep 17 00:00:00 2001 From: Travis Kessler Date: Wed, 25 Dec 2019 16:56:00 -0500 Subject: [PATCH 17/40] ML backend now uses PyTorch instead of Keras/TensorFlow --- ecnet/models/mlp.py | 252 +++++++++++++++++++++++++++++--------------- setup.py | 43 +++++--- 2 files changed, 199 insertions(+), 96 deletions(-) diff --git a/ecnet/models/mlp.py b/ecnet/models/mlp.py index 2ef2eab..175897c 100644 --- a/ecnet/models/mlp.py +++ b/ecnet/models/mlp.py @@ -10,51 +10,46 @@ # Stdlib imports from re import compile, IGNORECASE -from os import devnull, environ -import sys # 3rd party imports -from tensorflow import get_default_graph, logging -from numpy import array -stderr = sys.stderr -sys.stderr = open(devnull, 'w') -from keras.backend import clear_session, reset_uids -from keras.callbacks import EarlyStopping -from keras.layers import Dense -from keras.losses import mean_squared_error -from keras.metrics import mae -from keras.models import load_model, Sequential -from keras.optimizers import Adam -sys.stderr = stderr +from numpy import arange, array, asarray +from numpy.random import choice +from torch import load, relu, save, sigmoid, stack, tensor +from torch.nn import Linear, Module, MSELoss +from torch.optim import Adam # ECNet imports from ecnet.utils.logging import logger -environ['TF_CPP_MIN_LOG_LEVEL'] = '2' -logging.set_verbosity(logging.ERROR) +ECNET_EXT = compile(r'.*\.ecnet', flags=IGNORECASE) -H5_EXT = compile(r'.*\.h5', flags=IGNORECASE) +def check_ext(filename: str): + '''Ensures the supplied filename has a `.ecnet` extension -class MultilayerPerceptron: + Args: + filename (str): supplied filename + ''' - def __init__(self, filename: str='model.h5'): + if ECNET_EXT.match(filename) is None: + raise ValueError('Invalid filename/extension, must be `.ecnet`: {}' + .format(filename)) + + +class MultilayerPerceptron(Module): + + def __init__(self, filename: str='model.ecnet'): '''MultilayerPerceptron object: fits neural network to supplied inputs and targets Args: - filename (str): path to model save location (.h5 extension) + filename (str): path to model save location (.ecnet extension) ''' - if H5_EXT.match(filename) is None: - raise ValueError( - 'Invalid filename/extension, must be `.h5`: {}'.format( - filename - ) - ) + super(MultilayerPerceptron, self).__init__() + check_ext(filename) self._filename = filename - clear_session() - self._model = Sequential(name=filename.lower().replace('.h5', '')) + self._layers = [] def add_layer(self, num_neurons: int, activation: str, input_dim: int=None): @@ -62,22 +57,39 @@ def add_layer(self, num_neurons: int, activation: str, Args: num_neurons (int): number of neurons for the layer - activation (str): activation function for the layer (see Keras - activation function documentation) + activation (str): activation function for the layer (`sigmoid`, + `relu`, `linear`) input_dim (int): if not None (input layer), specifies input dimensionality ''' - self._model.add(Dense( - units=num_neurons, - activation=activation, - input_shape=(input_dim,) + if activation == 'sigmoid' or activation == sigmoid: + activation = sigmoid + elif activation == 'relu' or activation == relu: + activation = relu + elif activation == 'linear' or activation is None: + activation = None + else: + raise ValueError('Unknown activation: {}'.format(activation)) + if input_dim is not None: + self._layers.append(( + input_dim, num_neurons, activation + )) + else: + if len(self._layers) == 0: + raise AttributeError('Must include input dimensionality for ' + 'first layer') + self._layers.append(( + self._layers[-1][1], num_neurons, activation + )) + setattr(self, '_l{}'.format(len(self._layers) - 1), Linear( + self._layers[-1][0], self._layers[-1][1] )) def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None, epochs: int=1500, lr: float=0.001, beta_1: float=0.9, beta_2: float=0.999, epsilon: float=0.0000001, decay: float=0.0, - v: int=0, batch_size: int=32): + v: int=0, batch_size: int=32, patience: int=32) -> list: '''Fits neural network to supplied inputs and targets Args: @@ -96,48 +108,73 @@ def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None, decay (float): learning rate decay for Adam optimizer v (int): verbose training, `0` for no printing, `1` for printing batch_size (int): number of learning samples per batch + patience (int): if performing periodic validation, will wait this + many epochs for a better validation loss; will terminate + training if better loss not found + + Returns: + list: list of tuples, (train loss, valid loss) at each epoch; if + not performing periodic validation, index 1 of tuple is None ''' - self._model.compile( - loss=mean_squared_error, - optimizer=Adam( - lr=lr, - beta_1=beta_1, - beta_2=beta_2, - epsilon=epsilon, - decay=decay - ), - metrics=[mae] + criterion = MSELoss() + optimizer = Adam( + self.parameters(), + lr, + (beta_1, beta_2), + epsilon, + decay ) + losses = [] + trained_epochs = epochs + if v_x is not None and v_y is not None: - history = self._model.fit( - l_x, - l_y, - validation_data=(v_x, v_y), - callbacks=[EarlyStopping( - monitor='val_loss', - patience=250, - verbose=v, - mode='min', - restore_best_weights=True - )], - epochs=epochs, - verbose=v, - batch_size=batch_size - ) - epochs = len(history.history['loss']) + best_model_params = self.state_dict() + best_valid_loss = None + valid_count = 0 + for e in range(epochs): + train_x, train_y = self._batch(l_x, l_y, batch_size) + valid_x, valid_y = self._batch(v_x, v_y, batch_size) + optimizer.zero_grad() + train_output = self(train_x) + train_loss = criterion(train_output, train_y) + train_loss.backward() + optimizer.step() + valid_output = self(valid_x) + valid_loss = criterion(valid_output, valid_y) + if v == 1: + logger.log('debug', 'Epoch {} | Training Loss {} | ' + 'Validation Loss {}'.format(e + 1, train_loss, + valid_loss), call_loc='MLP') + losses.append((train_loss.item(), valid_loss.item())) + if best_valid_loss is None or valid_loss < best_valid_loss: + best_model_params = self.state_dict() + best_valid_loss = valid_loss + valid_count = 0 + else: + valid_count += 1 + if valid_count >= patience: + trained_epochs = e + 1 + break + self.load_state_dict(best_model_params) + else: - self._model.fit( - l_x, - l_y, - epochs=epochs, - verbose=v, - batch_size=batch_size - ) - - logger.log('debug', 'Training complete after {} epochs'.format(epochs), - call_loc='MLP') + for e in range(epochs): + train_x, train_y = self._batch(l_x, l_y, batch_size) + optimizer.zero_grad() + output = self(train_x) + loss = criterion(output, train_y) + if v == 1: + logger.log('debug', 'Epoch {} | Training Loss {}' + .format(e + 1, loss), call_loc='MLP') + losses.append((loss.item(), None)) + loss.backward() + optimizer.step() + + logger.log('debug', 'Training complete after {} epochs' + .format(trained_epochs), call_loc='MLP') + return losses def use(self, x: array) -> array: '''Uses neural network to predict values for supplied data @@ -149,11 +186,10 @@ def use(self, x: array) -> array: numpy.array: predictions ''' - with get_default_graph().as_default(): - return self._model.predict(x) + return asarray(self(tensor(x).float()).tolist()) def save(self, filename: str=None): - '''Saves neural network to .h5 file + '''Saves neural network to specified file filename (str): if None, uses MultilayerPerceptron._filename; otherwise, saves to this file @@ -161,25 +197,73 @@ def save(self, filename: str=None): if filename is None: filename = self._filename - if H5_EXT.match(filename) is None: - raise ValueError( - 'Invalid filename/extension, must be `.h5`: {}'.format( - filename - ) - ) - self._model.save(filename) + check_ext(filename) + save({ + 'layers': self._layers, + 'state_dict': self.state_dict() + }, filename) logger.log('debug', 'Model saved to {}'.format(filename), call_loc='MLP') def load(self, filename: str=None): - '''Loads neural network from .h5 file + '''Loads neural network from specified file Args: - filename (str): path to .h5 model file + filename (str): if None, uses MultilayerPerceptron._filename; + otherwise, loads from this file ''' if filename is None: filename = self._filename - self._model = load_model(filename) + check_ext(filename) + file_contents = load(filename) + layers = file_contents['layers'] + self._layers = [] + for idx, l in enumerate(layers): + if idx == 0: + self.add_layer(l[1], l[2], l[0]) + else: + self.add_layer(l[1], l[2]) + self.load_state_dict(file_contents['state_dict']) logger.log('debug', 'Model loaded from {}'.format(filename), call_loc='MLP') + + def forward(self, x: tensor) -> tensor: + '''Used by torch.nn.Module to perform forward pass through neural + network + + Args: + x (torch.tensor): data to pass through neural network + + Returns: + tensor: final layer's output + ''' + + for idx, layer in enumerate(self._layers): + if layer[2] is None: + x = getattr(self, '_l{}'.format(idx))(x) + else: + x = layer[2](getattr(self, '_l{}'.format(idx))(x)) + return x + + @staticmethod + def _batch(x: array, y: array, batch_size: int) -> array: + '''Returns a batch with specified size given input/target data + + Args: + x (np.array): input data + y (np.array): target data, same # samples as input data + batch_size (int): size of the batch + + Returns: + tuple: (np.array, np.array), each array of size batch_size + ''' + + if batch_size >= len(x): + x_b = tensor(x).float() + y_b = tensor(y).float() + else: + idxs = choice(arange(len(x)), batch_size, replace=False) + x_b = tensor(x[idxs]).float() + y_b = tensor(y[idxs]).float() + return (x_b, y_b) diff --git a/setup.py b/setup.py index 75434fb..0e46df7 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,33 @@ from setuptools import setup, find_packages +from platform import system, mac_ver + +install_requires = [ + 'alvadescpy==0.1.0', + 'colorlogging==0.3.5', + 'ecabc==2.2.3', + 'matplotlib==3.1.2', + 'numpy==1.16.4', + 'padelpy==0.1.6', + 'pyyaml==5.1.1', + 'scikit-learn==0.21.2' +] + +s = system() +if s == 'Windows': + install_requires.append( + 'torch @ https://download.pytorch.org/whl/cpu/torch-1.3.1%2Bcpu-cp37-cp37m-win_amd64.whl' + ) +elif s == 'Linux': + if mac_ver()[0] != '': + install_requires.append( + 'torch @ https://download.pytorch.org/whl/cpu/torch-1.3.1-cp37-none-macosx_10_7_x86_64.whl' + ) + else: + install_requires.append( + 'torch @ https://download.pytorch.org/whl/cpu/torch-1.3.1%2Bcpu-cp37-cp37m-linux_x86_64.whl' + ) +else: + raise OSError('Unsupported OS: {}'.format(s)) setup( name='ecnet', @@ -11,17 +40,7 @@ ' Hunter_Mack@uml.edu', license='MIT', packages=find_packages(), - install_requires=[ - 'alvadescpy==0.1.0', - 'colorlogging==0.3.5', - 'ecabc==2.2.3', - 'keras==2.2.4', - 'matplotlib==3.1.0', - 'numpy==1.16.4', - 'padelpy==0.1.6', - 'pyyaml==5.1.1', - 'scikit-learn==0.21.2', - 'tensorflow==1.13.1' - ], + python_requires='~=3.7', + install_requires=install_requires, zip_safe=False ) From 7efff7f5139afa677eba15a08220b48023396284 Mon Sep 17 00:00:00 2001 From: Travis Kessler Date: Wed, 25 Dec 2019 16:58:17 -0500 Subject: [PATCH 18/40] Update to default arguments according to PyTorch implementation --- ecnet/server.py | 12 ++++++------ ecnet/tasks/training.py | 6 +++--- ecnet/utils/server_utils.py | 18 +++++++++--------- ecnet/workflows/ecrl_workflow.py | 8 ++++---- ecnet/workflows/workflow_utils.py | 4 ++-- 5 files changed, 24 insertions(+), 24 deletions(-) diff --git a/ecnet/server.py b/ecnet/server.py index a93656c..3528487 100644 --- a/ecnet/server.py +++ b/ecnet/server.py @@ -187,7 +187,7 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int, def train(self, shuffle: str=None, split: list=None, retrain: bool=False, validate: bool=False, selection_set: str=None, - selection_fn: str='rmse', model_filename: str='model.h5'): + selection_fn: str='rmse', model_filename: str='model.ecnet'): '''Trains neural network(s) using currently-loaded data; single NN if no project is created, all candidates if created @@ -203,7 +203,7 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, set; `learn`, `valid`, `train`, `test`, None (all data) selection_fn (str): candidates are selected based on this error metric; `rmse`, `mean_abs_error`, `med_abs_error` - model_filename (str): if project not created, saves Keras h5 model + model_filename (str): if project not created, saves `.ecnet` file here ''' @@ -237,7 +237,7 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, ) def use(self, dset: str=None, output_filename: str=None, - model_filename: str='model.h5') -> list: + model_filename: str='model.ecnet') -> list: '''Uses trained neural network(s) to predict for specified set; single NN if no project created, best pool candidates if created @@ -245,7 +245,7 @@ def use(self, dset: str=None, output_filename: str=None, dset (str): set to predict for; `learn`, `valid`, `train`, `test`, None (all sets) output_filename (str): if supplied, saves results to this CSV file - model_filename (str): if supplied, use specified .h5 model file + model_filename (str): if supplied, use specified .ecnet model file Returns: list: list of results for specified set @@ -268,7 +268,7 @@ def use(self, dset: str=None, output_filename: str=None, return results def errors(self, *args, dset: str=None, - model_filename: str='model.h5') -> dict: + model_filename: str='model.ecnet') -> dict: '''Obtains various errors for specified set Args: @@ -276,7 +276,7 @@ def errors(self, *args, dset: str=None, `med_abs_error`, `r2` dset (str): set to obtain errors for; `learn`, `valid`, `train`, `test`, None (all sets) - model_filename (str): if specified, uses .h5 model file for error + model_filename (str): if specified, uses .ecnet model file for error calculations Returns: diff --git a/ecnet/tasks/training.py b/ecnet/tasks/training.py index 606054d..e47fa83 100644 --- a/ecnet/tasks/training.py +++ b/ecnet/tasks/training.py @@ -72,7 +72,7 @@ def train_project(prj_name: str, num_pools: int, num_candidates: int, candidate, model=True ) - save_df(df, filename.replace('model.h5', 'data.d')) + save_df(df, filename.replace('model.ecnet', 'data.d')) if num_processes > 1: pool_errors[pool].append(train_pool.apply_async( @@ -108,6 +108,6 @@ def train_project(prj_name: str, num_pools: int, num_candidates: int, pool_fp = get_candidate_path(prj_name, p_idx, p_best=True) resave_model(candidate_fp, pool_fp) resave_df( - candidate_fp.replace('model.h5', 'data.d'), - pool_fp.replace('model.h5', 'data.d') + candidate_fp.replace('model.ecnet', 'data.d'), + pool_fp.replace('model.ecnet', 'data.d') ) diff --git a/ecnet/utils/server_utils.py b/ecnet/utils/server_utils.py index f5312cc..e59f317 100644 --- a/ecnet/utils/server_utils.py +++ b/ecnet/utils/server_utils.py @@ -81,13 +81,13 @@ def default_config() -> dict: def get_candidate_path(prj: str, pool: int, candidate: int=None, model: bool=False, p_best: bool=False) -> str: - '''Get path to various states of model.h5 files + '''Get path to various states of model.ecnet files Args: prj (str): project name pool (int): pool number candidate (int): candidate number - model (bool): if True, appends `model.h5` to path + model (bool): if True, appends `model.ecnet` to path p_best (bool): if True, removes candidate folder from path Returns: @@ -99,13 +99,13 @@ def get_candidate_path(prj: str, pool: int, candidate: int=None, prj, 'pool_{}'.format(pool), 'candidate_{}'.format(candidate), - 'model.h5' + 'model.ecnet' ) elif p_best: return path.join( prj, 'pool_{}'.format(pool), - 'model.h5' + 'model.ecnet' ) else: return path.join( @@ -287,7 +287,7 @@ def resave_df(old: str, new: str): def resave_model(old: str, new: str): - '''Resaves .h5 model file + '''Resaves .ecnet model file Args: old (str): path to existing file @@ -366,7 +366,7 @@ def save_project(prj_name: str, filename: str, config_filename: str, def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, - retrain: bool=False, filename: str='model.h5', + retrain: bool=False, filename: str='model.ecnet', validate: bool=True, save: bool=True) -> float: '''Trains neural network @@ -378,7 +378,7 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, eval_fn (str): error function to evaluate the model; `rmse`, `mean_abs_error`, `med_abs_error` retrain (bool): if True, opens model for additional training - filename (str): path to .h5 model file, loads from this if retraining + filename (str): path to .ecnet model file, loads from this if retraining save (bool): if True, saves model to `filename` Returns: @@ -431,14 +431,14 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, def use_model(sets: PackagedData, dset: str, - filename: str='model.h5') -> array: + filename: str='model.ecnet') -> array: '''Uses existing model to predict data Args: sets (ecnet.utils.data_utils.PackagedData): data sets dset (str): set to predict for; `learn`, `valid`, `train`, `test`, None (all sets) - filename (str): path to existing .h5 model file + filename (str): path to existing .ecnet model file Returns: numpy.array: predicted data diff --git a/ecnet/workflows/ecrl_workflow.py b/ecnet/workflows/ecrl_workflow.py index 3096f46..981fe40 100644 --- a/ecnet/workflows/ecrl_workflow.py +++ b/ecnet/workflows/ecrl_workflow.py @@ -116,13 +116,13 @@ def create_model(prop_abvr: str, smiles: list=None, targets: list=None, df.create_sets() sets = df.package_sets() _ = train_model(sets, config, None, 'rmse', False, - db_name.replace('.csv', '.h5'), True, True) + db_name.replace('.csv', '.ecnet'), True, True) logger.log('info', 'Generated ANN', 'WORKFLOW') logger.log('info', 'Measuring ANN performance...', 'WORKFLOW') - preds_learn = use_model(sets, 'learn', db_name.replace('.csv', '.h5')) - preds_valid = use_model(sets, 'valid', db_name.replace('.csv', '.h5')) - preds_test = use_model(sets, 'test', db_name.replace('.csv', '.h5')) + preds_learn = use_model(sets, 'learn', db_name.replace('.csv', '.ecnet')) + preds_valid = use_model(sets, 'valid', db_name.replace('.csv', '.ecnet')) + preds_test = use_model(sets, 'test', db_name.replace('.csv', '.ecnet')) learn_mae = calc_med_abs_error(preds_learn, sets.learn_y) learn_r2 = calc_r2(preds_learn, sets.learn_y) valid_mae = calc_med_abs_error(preds_valid, sets.valid_y) diff --git a/ecnet/workflows/workflow_utils.py b/ecnet/workflows/workflow_utils.py index f9ab635..acfc781 100644 --- a/ecnet/workflows/workflow_utils.py +++ b/ecnet/workflows/workflow_utils.py @@ -82,13 +82,13 @@ def find_optimal_num_inputs(db_name: str, eval_set: str, if num_processes > 1: errors.append([d_idx, train_pool.apply_async( train_model, [ - sets, conf, eval_set, 'med_abs_error', False, '_.h5', + sets, conf, eval_set, 'med_abs_error', False, '_.ecnet', False, False ] )]) else: errors.append([d_idx, train_model( - sets, conf, eval_set, 'med_abs_error', False, '_.h5', + sets, conf, eval_set, 'med_abs_error', False, '_.ecnet', False, False )]) From 88db3e54ea94f429c89ba831171a96d63c02a00f Mon Sep 17 00:00:00 2001 From: Travis Kessler Date: Wed, 25 Dec 2019 16:58:38 -0500 Subject: [PATCH 19/40] Ensuring UTF-8 encoding --- ecnet/utils/data_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ecnet/utils/data_utils.py b/ecnet/utils/data_utils.py index 3f8a871..cf4e29d 100644 --- a/ecnet/utils/data_utils.py +++ b/ecnet/utils/data_utils.py @@ -371,7 +371,7 @@ def save(self, filename: str): data_rows.append(data_row) rows.extend(sorted(data_rows, key=lambda x: x[0])) - with open(filename, 'w') as csv_file: + with open(filename, 'w', encoding='utf8') as csv_file: wr = writer(csv_file, quoting=QUOTE_ALL, lineterminator='\n') for row in rows: wr.writerow(row) @@ -437,7 +437,7 @@ def save_results(results: list, dset: str, df: DataFrame, filename: str): data_rows.append(data_row) rows.extend(sorted(data_rows, key=lambda x: x[0])) - with open(filename, 'w') as csv_file: + with open(filename, 'w', encoding='utf8') as csv_file: wr = writer(csv_file, quoting=QUOTE_ALL, lineterminator='\n') for row in rows: wr.writerow(row) From c1d59d116a1a7099805e955c9e7648b2117ad2ea Mon Sep 17 00:00:00 2001 From: Travis Kessler Date: Wed, 25 Dec 2019 16:59:25 -0500 Subject: [PATCH 20/40] Update to unit tests per PyTorch implementation --- tests/models/test_mlp.py | 2 +- tests/server/test_server.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/models/test_mlp.py b/tests/models/test_mlp.py index 51fd99f..18766eb 100644 --- a/tests/models/test_mlp.py +++ b/tests/models/test_mlp.py @@ -27,7 +27,7 @@ def test_mlp(self): results = mlp_saved.use(array([[1, 1, 1], [0, 0, 0]])) self.assertAlmostEqual(results[0][0], 1, 3) self.assertAlmostEqual(results[1][0], 0, 3) - remove('model.h5') + remove('model.ecnet') if __name__ == '__main__': diff --git a/tests/server/test_server.py b/tests/server/test_server.py index 1653310..7730309 100644 --- a/tests/server/test_server.py +++ b/tests/server/test_server.py @@ -58,14 +58,14 @@ def test_train_project(self): self.assertTrue(exists(join( 'test_project', 'pool_{}'.format(pool), - 'model.h5' + 'model.ecnet' ))) for candidate in range(2): self.assertTrue(exists(join( 'test_project', 'pool_{}'.format(pool), 'candidate_{}'.format(candidate), - 'model.h5' + 'model.ecnet' ))) remove('config.yml') rmtree('test_project') @@ -109,14 +109,14 @@ def test_multiprocessing_train(self): self.assertTrue(exists(join( 'test_project', 'pool_{}'.format(pool), - 'model.h5' + 'model.ecnet' ))) for candidate in range(4): self.assertTrue(exists(join( 'test_project', 'pool_{}'.format(pool), 'candidate_{}'.format(candidate), - 'model.h5' + 'model.ecnet' ))) remove('config.yml') rmtree('test_project') From 03f1aba60b1060d580003b3c5aaa3be1adcea39d Mon Sep 17 00:00:00 2001 From: Travis Kessler Date: Wed, 25 Dec 2019 17:45:34 -0500 Subject: [PATCH 21/40] Version bump: 3.2.3 -> 3.3.0 --- ecnet/__init__.py | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ecnet/__init__.py b/ecnet/__init__.py index af01be1..b6bce23 100644 --- a/ecnet/__init__.py +++ b/ecnet/__init__.py @@ -1,2 +1,2 @@ from ecnet.server import Server -__version__ = '3.2.3' +__version__ = '3.3.0' diff --git a/setup.py b/setup.py index 0e46df7..c619b93 100644 --- a/setup.py +++ b/setup.py @@ -31,7 +31,7 @@ setup( name='ecnet', - version='3.2.3', + version='3.3.0', description='UMass Lowell Energy and Combustion Research Laboratory Neural' ' Network Software', url='https://github.com/ecrl/ecnet', From 1f1fd6212d96c455ad24b5ee0e33344d7c4d7e9e Mon Sep 17 00:00:00 2001 From: Travis Kessler Date: Wed, 25 Dec 2019 17:46:44 -0500 Subject: [PATCH 22/40] Added verbose arg to server->utils->mlp --- ecnet/server.py | 8 ++++++-- ecnet/utils/server_utils.py | 10 +++++++--- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/ecnet/server.py b/ecnet/server.py index 3528487..71c8279 100644 --- a/ecnet/server.py +++ b/ecnet/server.py @@ -187,7 +187,8 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int, def train(self, shuffle: str=None, split: list=None, retrain: bool=False, validate: bool=False, selection_set: str=None, - selection_fn: str='rmse', model_filename: str='model.ecnet'): + selection_fn: str='rmse', model_filename: str='model.ecnet', + verbose=0): '''Trains neural network(s) using currently-loaded data; single NN if no project is created, all candidates if created @@ -205,6 +206,8 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, metric; `rmse`, `mean_abs_error`, `med_abs_error` model_filename (str): if project not created, saves `.ecnet` file here + verbose (int): 1 to display loss at each epoch, 0 otherwise (single + model only) ''' if self._prj_name is None: @@ -216,7 +219,8 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, selection_fn, retrain, model_filename, - validate + validate, + verbose=verbose ) else: diff --git a/ecnet/utils/server_utils.py b/ecnet/utils/server_utils.py index e59f317..f4c5737 100644 --- a/ecnet/utils/server_utils.py +++ b/ecnet/utils/server_utils.py @@ -367,7 +367,7 @@ def save_project(prj_name: str, filename: str, config_filename: str, def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, retrain: bool=False, filename: str='model.ecnet', - validate: bool=True, save: bool=True) -> float: + validate: bool=True, save: bool=True, verbose: int=0) -> float: '''Trains neural network Args: @@ -380,6 +380,8 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, retrain (bool): if True, opens model for additional training filename (str): path to .ecnet model file, loads from this if retraining save (bool): if True, saves model to `filename` + verbose (int): 1 to display loss at each epoch, 0 otherwise (single + model only) Returns: float: error of evaluated set @@ -407,7 +409,8 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, beta_2=vars['beta_2'], epsilon=vars['epsilon'], decay=vars['decay'], - batch_size=vars['batch_size'] + batch_size=vars['batch_size'], + v=verbose ) else: model.fit( @@ -419,7 +422,8 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, beta_2=vars['beta_2'], epsilon=vars['epsilon'], decay=vars['decay'], - batch_size=vars['batch_size'] + batch_size=vars['batch_size'], + v=verbose ) if save: model.save() From 61084e7b2ad4b2de2d033af533d55e6dc62083e9 Mon Sep 17 00:00:00 2001 From: Travis Kessler Date: Wed, 25 Dec 2019 17:47:24 -0500 Subject: [PATCH 23/40] Update to default patience arg (TODO: tune it) --- ecnet/models/mlp.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ecnet/models/mlp.py b/ecnet/models/mlp.py index 175897c..b1dc54b 100644 --- a/ecnet/models/mlp.py +++ b/ecnet/models/mlp.py @@ -89,7 +89,7 @@ def add_layer(self, num_neurons: int, activation: str, def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None, epochs: int=1500, lr: float=0.001, beta_1: float=0.9, beta_2: float=0.999, epsilon: float=0.0000001, decay: float=0.0, - v: int=0, batch_size: int=32, patience: int=32) -> list: + v: int=0, batch_size: int=32, patience: int=128) -> list: '''Fits neural network to supplied inputs and targets Args: From 366ee613eb312579bf8100661fa8c6174c3ed936 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Mon, 30 Dec 2019 14:59:06 -0500 Subject: [PATCH 24/40] Torch installation now uses dependency_links arg --- setup.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/setup.py b/setup.py index c619b93..9f18a90 100644 --- a/setup.py +++ b/setup.py @@ -2,6 +2,7 @@ from platform import system, mac_ver install_requires = [ + 'torch', 'alvadescpy==0.1.0', 'colorlogging==0.3.5', 'ecabc==2.2.3', @@ -14,18 +15,12 @@ s = system() if s == 'Windows': - install_requires.append( - 'torch @ https://download.pytorch.org/whl/cpu/torch-1.3.1%2Bcpu-cp37-cp37m-win_amd64.whl' - ) + dependency_links = ['https://download.pytorch.org/whl/cpu/torch-1.3.1%2Bcpu-cp37-cp37m-win_amd64.whl'] elif s == 'Linux': if mac_ver()[0] != '': - install_requires.append( - 'torch @ https://download.pytorch.org/whl/cpu/torch-1.3.1-cp37-none-macosx_10_7_x86_64.whl' - ) + dependency_links = ['https://download.pytorch.org/whl/cpu/torch-1.3.1-cp37-none-macosx_10_7_x86_64.whl'] else: - install_requires.append( - 'torch @ https://download.pytorch.org/whl/cpu/torch-1.3.1%2Bcpu-cp37-cp37m-linux_x86_64.whl' - ) + dependency_links = ['https://download.pytorch.org/whl/cpu/torch-1.3.1%2Bcpu-cp37-cp37m-linux_x86_64.whl'] else: raise OSError('Unsupported OS: {}'.format(s)) @@ -42,5 +37,6 @@ packages=find_packages(), python_requires='~=3.7', install_requires=install_requires, + dependency_links=dependency_links, zip_safe=False ) From 52f8a299ff35034d4412e45bb54fa83ea3620ddc Mon Sep 17 00:00:00 2001 From: tjkessler Date: Tue, 31 Dec 2019 13:26:52 -0500 Subject: [PATCH 25/40] Training single model now returns learn/valid losses --- ecnet/server.py | 10 ++++++++-- ecnet/tasks/training.py | 4 ++-- ecnet/tasks/tuning.py | 20 ++++++++++++++------ ecnet/utils/server_utils.py | 21 ++++++++++++--------- ecnet/workflows/ecrl_workflow.py | 21 ++++++++++++++++++--- ecnet/workflows/workflow_utils.py | 4 ++-- 6 files changed, 56 insertions(+), 24 deletions(-) diff --git a/ecnet/server.py b/ecnet/server.py index 71c8279..478343e 100644 --- a/ecnet/server.py +++ b/ecnet/server.py @@ -188,7 +188,7 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int, def train(self, shuffle: str=None, split: list=None, retrain: bool=False, validate: bool=False, selection_set: str=None, selection_fn: str='rmse', model_filename: str='model.ecnet', - verbose=0): + verbose=0) -> list: '''Trains neural network(s) using currently-loaded data; single NN if no project is created, all candidates if created @@ -208,11 +208,15 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, here verbose (int): 1 to display loss at each epoch, 0 otherwise (single model only) + + Returns: + list: if training single model, returns list of learn/valid losses, + else None ''' if self._prj_name is None: logger.log('info', 'Training single model', call_loc='TRAIN') - train_model( + _, losses = train_model( self._sets, self._vars, selection_set, @@ -222,6 +226,7 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, validate, verbose=verbose ) + return losses else: train_project( @@ -239,6 +244,7 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, selection_fn, self._num_processes ) + return None def use(self, dset: str=None, output_filename: str=None, model_filename: str='model.ecnet') -> list: diff --git a/ecnet/tasks/training.py b/ecnet/tasks/training.py index e47fa83..c09db26 100644 --- a/ecnet/tasks/training.py +++ b/ecnet/tasks/training.py @@ -83,7 +83,7 @@ def train_project(prj_name: str, num_pools: int, num_candidates: int, pool_errors[pool].append(train_model( sets, vars, selection_set, selection_fn, retrain, filename, validate - )) + )[0]) if shuffle is not None: df.shuffle(sets=shuffle, split=split) @@ -93,7 +93,7 @@ def train_project(prj_name: str, num_pools: int, num_candidates: int, train_pool.close() train_pool.join() for p_idx, pool in enumerate(pool_errors): - pool_errors[p_idx] = [e.get() for e in pool] + pool_errors[p_idx] = [e.get()[0] for e in pool] logger.log('debug', 'Pool errors: {}'.format(pool_errors), call_loc='TRAIN') diff --git a/ecnet/tasks/tuning.py b/ecnet/tasks/tuning.py index c8681dd..4dd328c 100644 --- a/ecnet/tasks/tuning.py +++ b/ecnet/tasks/tuning.py @@ -73,8 +73,10 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int, } value_ranges = [ - ('float', (1e-9, 1e-4)), - ('float', (1e-5, 0.1)) + ('float', (1e-9, 1e-4)), # Learning rate decay + ('float', (1e-5, 0.1)), # Learning rate + ('int', (1, len(df))), # Batch size + ('int', (1, 1600)) # Patience ] for _ in range(len(vars['hidden_layers'])): @@ -101,14 +103,18 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int, abc.best_performer[2], { 'decay': abc.best_performer[1][0], 'learning_rate': abc.best_performer[1][1], - 'hidden_layers': abc.best_performer[1][2:] + 'batch_size': abc.best_performer[1][2], + 'patience': abc.best_performer[1][3], + 'hidden_layers': abc.best_performer[1][4:] } ), call_loc='TUNE') params = abc.best_performer[1] vars['decay'] = params[0] vars['learning_rate'] = params[1] + vars['batch_size'] = params[2] + vars['patience'] = params[3] for l_idx in range(len(vars['hidden_layers'])): - vars['hidden_layers'][l_idx][0] = params[2 + l_idx] + vars['hidden_layers'][l_idx][0] = params[4 + l_idx] return vars @@ -126,10 +132,12 @@ def tune_fitness_function(params: dict, **kwargs): vars = default_config() vars['decay'] = params[0] vars['learning_rate'] = params[1] + vars['batch_size'] = params[2] + vars['patience'] = params[3] vars['hidden_layers'] = kwargs['hidden_layers'] vars['epochs'] = kwargs['epochs'] for l_idx in range(len(vars['hidden_layers'])): - vars['hidden_layers'][l_idx][0] = params[2 + l_idx] + vars['hidden_layers'][l_idx][0] = params[4 + l_idx] df = kwargs['df'] if kwargs['shuffle'] is not None: @@ -137,4 +145,4 @@ def tune_fitness_function(params: dict, **kwargs): sets = df.package_sets() return train_model(sets, vars, kwargs['eval_set'], kwargs['eval_fn'], - validate=kwargs['validate'], save=False) + validate=kwargs['validate'], save=False)[0] diff --git a/ecnet/utils/server_utils.py b/ecnet/utils/server_utils.py index f4c5737..cc35981 100644 --- a/ecnet/utils/server_utils.py +++ b/ecnet/utils/server_utils.py @@ -75,7 +75,8 @@ def default_config() -> dict: [32, 'relu'] ], 'output_activation': 'linear', - 'batch_size': 32 + 'batch_size': 32, + 'patience': 128 } @@ -367,7 +368,7 @@ def save_project(prj_name: str, filename: str, config_filename: str, def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, retrain: bool=False, filename: str='model.ecnet', - validate: bool=True, save: bool=True, verbose: int=0) -> float: + validate: bool=True, save: bool=True, verbose: int=0) -> tuple: '''Trains neural network Args: @@ -384,7 +385,7 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, model only) Returns: - float: error of evaluated set + tuple: (error of evaluated set, list of learn/valid losses) ''' model = MultilayerPerceptron(filename=filename) @@ -398,7 +399,7 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, model.add_layer(layer[0], layer[1]) model.add_layer(len(sets.learn_y[0]), vars['output_activation']) if validate: - model.fit( + losses = model.fit( sets.learn_x, sets.learn_y, sets.valid_x, @@ -410,10 +411,11 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, epsilon=vars['epsilon'], decay=vars['decay'], batch_size=vars['batch_size'], - v=verbose + v=verbose, + patience=vars['patience'] ) else: - model.fit( + losses = model.fit( sets.learn_x, sets.learn_y, epochs=vars['epochs'], @@ -423,15 +425,16 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, epsilon=vars['epsilon'], decay=vars['decay'], batch_size=vars['batch_size'], - v=verbose + v=verbose, + patience=vars['patience'] ) if save: model.save() - return get_error( + return (get_error( model.use(get_x(sets, eval_set)), get_y(sets, eval_set), eval_fn - ) + ), losses) def use_model(sets: PackagedData, dset: str, diff --git a/ecnet/workflows/ecrl_workflow.py b/ecnet/workflows/ecrl_workflow.py index 981fe40..c6f1b33 100644 --- a/ecnet/workflows/ecrl_workflow.py +++ b/ecnet/workflows/ecrl_workflow.py @@ -108,6 +108,9 @@ def create_model(prop_abvr: str, smiles: list=None, targets: list=None, logger.log('info', '\tLearning rate: {}'.format(config['learning_rate']), 'WORKFLOW') logger.log('info', '\tLR decay: {}'.format(config['decay']), 'WORKFLOW') + logger.log('info', '\tBatch size: {}'.format(config['batch_size']), + 'WORKFLOW') + logger.log('info', '\tPatience: {}'.format(config['patience']), 'WORKFLOW') logger.log('info', '\tHidden layers: {}'.format(config['hidden_layers']), 'WORKFLOW') @@ -115,9 +118,21 @@ def create_model(prop_abvr: str, smiles: list=None, targets: list=None, logger.log('info', 'Generating ANN...', 'WORKFLOW') df.create_sets() sets = df.package_sets() - _ = train_model(sets, config, None, 'rmse', False, - db_name.replace('.csv', '.ecnet'), True, True) + _, losses = train_model(sets, config, None, 'rmse', False, + db_name.replace('.csv', '.ecnet'), True, True) logger.log('info', 'Generated ANN', 'WORKFLOW') + if create_plots: + learn_losses = [l[0] for l in losses] + valid_losses = [l[1] for l in losses] + x_vals = [i for i in range(len(learn_losses))] + plt.clf() + plt.rcParams['font.family'] = 'Times New Roman' + plt.plot(x_vals, learn_losses, c='blue', label='Learning Set') + plt.plot(x_vals, valid_losses, c='red', label='Validation Set') + plt.xlabel('Training Iterations (Epochs)') + plt.ylabel('Loss (Mean Squared Error)') + plt.legend(loc='lower left') + plt.savefig(db_name.replace('.csv', '_loss_graph.png')) logger.log('info', 'Measuring ANN performance...', 'WORKFLOW') preds_learn = use_model(sets, 'learn', db_name.replace('.csv', '.ecnet')) @@ -141,7 +156,7 @@ def create_model(prop_abvr: str, smiles: list=None, targets: list=None, logger.log('info', 'Creating parity plot...', 'WORKFLOW') plt.clf() parity_plot = ParityPlot( - '{} Parity Plot'.format(prop_abvr), + '', 'Experimental {} Value'.format(prop_abvr), 'Predicted {} Value'.format(prop_abvr) ) diff --git a/ecnet/workflows/workflow_utils.py b/ecnet/workflows/workflow_utils.py index acfc781..a60eba9 100644 --- a/ecnet/workflows/workflow_utils.py +++ b/ecnet/workflows/workflow_utils.py @@ -90,13 +90,13 @@ def find_optimal_num_inputs(db_name: str, eval_set: str, errors.append([d_idx, train_model( sets, conf, eval_set, 'med_abs_error', False, '_.ecnet', False, False - )]) + )[0]]) if num_processes > 1: train_pool.close() train_pool.join() for idx, err in enumerate(errors): - errors[idx][1] = err[1].get() + errors[idx][1] = err[1].get()[0] min_error = errors[0][1] opt_num_desc = 1 From 77c7772607b39b42c32fdad7887a46bb4d703fb6 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Tue, 31 Dec 2019 13:27:13 -0500 Subject: [PATCH 26/40] Updated unit test to include patience hyperparam --- tests/utils/test_server_utils.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/tests/utils/test_server_utils.py b/tests/utils/test_server_utils.py index e83c8f3..52da43f 100644 --- a/tests/utils/test_server_utils.py +++ b/tests/utils/test_server_utils.py @@ -40,7 +40,8 @@ def test_default_config(self): [32, 'relu'] ], 'output_activation': 'linear', - 'batch_size': 32 + 'batch_size': 32, + 'patience': 128 } ) @@ -136,7 +137,8 @@ def test_open_save_config(self): [32, 'relu'] ], 'output_activation': 'linear', - 'batch_size': 32 + 'batch_size': 32, + 'patience': 128 } ) remove('config.yml') @@ -162,7 +164,7 @@ def test_train_model(self): pd = df.package_sets() config = server_utils.default_config() config['epochs'] = 100 - r_squared = server_utils.train_model( + _ = server_utils.train_model( pd, config, 'test', 'r2', filename='test_train.ecnet' ) self.assertTrue(exists('test_train.ecnet')) From 35bdb5d3fcbddb59e6ea4342edea5b9b48da994f Mon Sep 17 00:00:00 2001 From: tjkessler Date: Thu, 2 Jan 2020 13:39:53 -0500 Subject: [PATCH 27/40] Range of patience for tuning: [64, 1024] --- ecnet/tasks/tuning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ecnet/tasks/tuning.py b/ecnet/tasks/tuning.py index 4dd328c..d742052 100644 --- a/ecnet/tasks/tuning.py +++ b/ecnet/tasks/tuning.py @@ -76,7 +76,7 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int, ('float', (1e-9, 1e-4)), # Learning rate decay ('float', (1e-5, 0.1)), # Learning rate ('int', (1, len(df))), # Batch size - ('int', (1, 1600)) # Patience + ('int', (64, 1024)) # Patience ] for _ in range(len(vars['hidden_layers'])): From 40a151fba221105de7ddf2718e2b6a40261930cd Mon Sep 17 00:00:00 2001 From: tjkessler Date: Thu, 2 Jan 2020 13:40:41 -0500 Subject: [PATCH 28/40] Batch size = max for finding optimal num inputs --- ecnet/workflows/workflow_utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ecnet/workflows/workflow_utils.py b/ecnet/workflows/workflow_utils.py index a60eba9..5ccfb75 100644 --- a/ecnet/workflows/workflow_utils.py +++ b/ecnet/workflows/workflow_utils.py @@ -59,6 +59,7 @@ def find_optimal_num_inputs(db_name: str, eval_set: str, conf['epochs'] = 300 df = DataFrame(db_name) df.create_sets() + conf['batch_size'] = len(df.learn_set) desc = limit_rforest(df, len(df._input_names), num_processes=num_processes, eval_set=eval_set) desc = [d[0] for d in desc] From f01b28a223d746b776901f19b96347596345c698 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Thu, 2 Jan 2020 15:37:06 -0500 Subject: [PATCH 29/40] Training now builds a 5x25 project --- ecnet/workflows/ecrl_workflow.py | 67 +++++++++++++------------------- 1 file changed, 27 insertions(+), 40 deletions(-) diff --git a/ecnet/workflows/ecrl_workflow.py b/ecnet/workflows/ecrl_workflow.py index c6f1b33..5d4221e 100644 --- a/ecnet/workflows/ecrl_workflow.py +++ b/ecnet/workflows/ecrl_workflow.py @@ -1,3 +1,4 @@ +from ecnet import Server from ecnet.tasks.tuning import tune_hyperparameters from ecnet.tools.database import create_db from ecnet.tools.plotting import ParityPlot @@ -74,7 +75,7 @@ def create_model(prop_abvr: str, smiles: list=None, targets: list=None, errors, desc = find_optimal_num_inputs(db_name, 'valid', num_processes) df = DataFrame(db_name) df.set_inputs(desc) - df.save(db_name) + df.save(db_name.replace('.csv', '_opt.csv')) logger.log('info', 'Found optimal number of inputs', 'WORKFLOW') logger.log('info', '\tNumber of inputs: {}'.format(len(df._input_names)), 'WORKFLOW') @@ -116,41 +117,27 @@ def create_model(prop_abvr: str, smiles: list=None, targets: list=None, # Create Model logger.log('info', 'Generating ANN...', 'WORKFLOW') - df.create_sets() - sets = df.package_sets() - _, losses = train_model(sets, config, None, 'rmse', False, - db_name.replace('.csv', '.ecnet'), True, True) - logger.log('info', 'Generated ANN', 'WORKFLOW') - if create_plots: - learn_losses = [l[0] for l in losses] - valid_losses = [l[1] for l in losses] - x_vals = [i for i in range(len(learn_losses))] - plt.clf() - plt.rcParams['font.family'] = 'Times New Roman' - plt.plot(x_vals, learn_losses, c='blue', label='Learning Set') - plt.plot(x_vals, valid_losses, c='red', label='Validation Set') - plt.xlabel('Training Iterations (Epochs)') - plt.ylabel('Loss (Mean Squared Error)') - plt.legend(loc='lower left') - plt.savefig(db_name.replace('.csv', '_loss_graph.png')) - + sv = Server(db_name.replace('.csv', '.yml'), num_processes=num_processes) + sv.load_data(db_name.replace('.csv', '_opt.csv')) + sv.create_project(db_name.replace('.csv', ''), 5, 25) + sv.train(validate=True, selection_set='valid', + selection_fn='med_abs_error') + logger.log('info', 'ANN Generated', 'WORKFLOW') logger.log('info', 'Measuring ANN performance...', 'WORKFLOW') - preds_learn = use_model(sets, 'learn', db_name.replace('.csv', '.ecnet')) - preds_valid = use_model(sets, 'valid', db_name.replace('.csv', '.ecnet')) - preds_test = use_model(sets, 'test', db_name.replace('.csv', '.ecnet')) - learn_mae = calc_med_abs_error(preds_learn, sets.learn_y) - learn_r2 = calc_r2(preds_learn, sets.learn_y) - valid_mae = calc_med_abs_error(preds_valid, sets.valid_y) - valid_r2 = calc_r2(preds_valid, sets.valid_y) - test_mae = calc_med_abs_error(preds_test, sets.test_y) - test_r2 = calc_r2(preds_test, sets.test_y) + preds_learn = sv.use(dset='learn') + preds_valid = sv.use(dset='valid') + preds_test = sv.use(dset='test') + learn_errors = sv.errors('r2', 'med_abs_error', dset='learn') + valid_errors = sv.errors('r2', 'med_abs_error', dset='valid') + test_errors = sv.errors('r2', 'med_abs_error', dset='test') logger.log('info', 'Measured ANN performance', 'WORKFLOW') logger.log('info', '\tLearning set:\t R2: {}\t MAE: {}'.format( - learn_r2, learn_mae), 'WORKFLOW') + learn_errors['r2'], learn_errors['med_abs_error']), 'WORKFLOW') logger.log('info', '\tValidation set:\t R2: {}\t MAE: {}'.format( - valid_r2, valid_mae), 'WORKFLOW') + valid_errors['r2'], valid_errors['med_abs_error']), 'WORKFLOW') logger.log('info', '\tTesting set:\t R2: {}\t MAE: {}'.format( - test_r2, test_mae), 'WORKFLOW') + test_errors['r2'], test_errors['med_abs_error']), 'WORKFLOW') + sv.save_project(del_candidates=True) if create_plots: logger.log('info', 'Creating parity plot...', 'WORKFLOW') @@ -160,16 +147,16 @@ def create_model(prop_abvr: str, smiles: list=None, targets: list=None, 'Experimental {} Value'.format(prop_abvr), 'Predicted {} Value'.format(prop_abvr) ) - parity_plot.add_series(sets.learn_y, preds_learn, 'Learning Set', + parity_plot.add_series(sv._sets.learn_y, preds_learn, 'Learning Set', 'blue') - parity_plot.add_series(sets.valid_y, preds_valid, 'Validation Set', + parity_plot.add_series(sv._sets.valid_y, preds_valid, 'Validation Set', 'green') - parity_plot.add_series(sets.test_y, preds_test, 'Test Set', 'red') - parity_plot.add_error_bars(test_mae, 'Test MAE') - parity_plot._add_label('Test $R^2$', test_r2) - parity_plot._add_label('Validation MAE', valid_mae) - parity_plot._add_label('Validation $R^2$', valid_r2) - parity_plot._add_label('Learning MAE', learn_mae) - parity_plot._add_label('Learning $R^2$', learn_r2) + parity_plot.add_series(sv._sets.test_y, preds_test, 'Test Set', 'red') + parity_plot.add_error_bars(test_errors['med_abs_error'], 'Test MAE') + parity_plot._add_label('Test $R^2$', test_errors['r2']) + parity_plot._add_label('Validation MAE', valid_errors['med_abs_error']) + parity_plot._add_label('Validation $R^2$', valid_errors['r2']) + parity_plot._add_label('Learning MAE', learn_errors['med_abs_error']) + parity_plot._add_label('Learning $R^2$', learn_errors['r2']) parity_plot.save(db_name.replace('.csv', '_parity.png')) logger.log('info', 'Created parity plot', 'WORKFLOW') From e5ada6ea4efd1e188054fe01451a3179c4a416f5 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Sun, 5 Jan 2020 13:39:11 -0500 Subject: [PATCH 30/40] Revert to TF, performance/compatability concerns --- ecnet/models/mlp.py | 259 ++++++++++++++------------------------------ setup.py | 38 ++----- 2 files changed, 96 insertions(+), 201 deletions(-) diff --git a/ecnet/models/mlp.py b/ecnet/models/mlp.py index b1dc54b..39c3a90 100644 --- a/ecnet/models/mlp.py +++ b/ecnet/models/mlp.py @@ -10,88 +10,73 @@ # Stdlib imports from re import compile, IGNORECASE +from os import devnull, environ +import sys # 3rd party imports -from numpy import arange, array, asarray -from numpy.random import choice -from torch import load, relu, save, sigmoid, stack, tensor -from torch.nn import Linear, Module, MSELoss -from torch.optim import Adam +from tensorflow import get_default_graph, logging +from numpy import array +stderr = sys.stderr +sys.stderr = open(devnull, 'w') +from keras.backend import clear_session, reset_uids +from keras.callbacks import EarlyStopping +from keras.layers import Dense +from keras.losses import mean_squared_error +from keras.metrics import mae +from keras.models import load_model, Sequential +from keras.optimizers import Adam +sys.stderr = stderr # ECNet imports from ecnet.utils.logging import logger -ECNET_EXT = compile(r'.*\.ecnet', flags=IGNORECASE) +environ['TF_CPP_MIN_LOG_LEVEL'] = '2' +logging.set_verbosity(logging.ERROR) +H5_EXT = compile(r'.*\.h5', flags=IGNORECASE) -def check_ext(filename: str): - '''Ensures the supplied filename has a `.ecnet` extension - Args: - filename (str): supplied filename - ''' +class MultilayerPerceptron: - if ECNET_EXT.match(filename) is None: - raise ValueError('Invalid filename/extension, must be `.ecnet`: {}' - .format(filename)) - - -class MultilayerPerceptron(Module): - - def __init__(self, filename: str='model.ecnet'): + def __init__(self, filename: str='model.h5'): '''MultilayerPerceptron object: fits neural network to supplied inputs and targets - Args: - filename (str): path to model save location (.ecnet extension) + filename (str): path to model save location (.h5 extension) ''' - super(MultilayerPerceptron, self).__init__() - check_ext(filename) + if H5_EXT.match(filename) is None: + raise ValueError( + 'Invalid filename/extension, must be `.h5`: {}'.format( + filename + ) + ) self._filename = filename - self._layers = [] + clear_session() + self._model = Sequential(name=filename.lower().replace('.h5', '')) def add_layer(self, num_neurons: int, activation: str, input_dim: int=None): '''Adds a fully-connected layer to the model - Args: num_neurons (int): number of neurons for the layer - activation (str): activation function for the layer (`sigmoid`, - `relu`, `linear`) + activation (str): activation function for the layer (see Keras + activation function documentation) input_dim (int): if not None (input layer), specifies input dimensionality ''' - if activation == 'sigmoid' or activation == sigmoid: - activation = sigmoid - elif activation == 'relu' or activation == relu: - activation = relu - elif activation == 'linear' or activation is None: - activation = None - else: - raise ValueError('Unknown activation: {}'.format(activation)) - if input_dim is not None: - self._layers.append(( - input_dim, num_neurons, activation - )) - else: - if len(self._layers) == 0: - raise AttributeError('Must include input dimensionality for ' - 'first layer') - self._layers.append(( - self._layers[-1][1], num_neurons, activation - )) - setattr(self, '_l{}'.format(len(self._layers) - 1), Linear( - self._layers[-1][0], self._layers[-1][1] + self._model.add(Dense( + units=num_neurons, + activation=activation, + input_shape=(input_dim,) )) def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None, epochs: int=1500, lr: float=0.001, beta_1: float=0.9, beta_2: float=0.999, epsilon: float=0.0000001, decay: float=0.0, - v: int=0, batch_size: int=32, patience: int=128) -> list: + v: int=0, batch_size: int=32): '''Fits neural network to supplied inputs and targets - Args: l_x (numpy.array): learning input data l_y (numpy.array): learning target data @@ -108,162 +93,86 @@ def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None, decay (float): learning rate decay for Adam optimizer v (int): verbose training, `0` for no printing, `1` for printing batch_size (int): number of learning samples per batch - patience (int): if performing periodic validation, will wait this - many epochs for a better validation loss; will terminate - training if better loss not found - - Returns: - list: list of tuples, (train loss, valid loss) at each epoch; if - not performing periodic validation, index 1 of tuple is None ''' - criterion = MSELoss() - optimizer = Adam( - self.parameters(), - lr, - (beta_1, beta_2), - epsilon, - decay + self._model.compile( + loss=mean_squared_error, + optimizer=Adam( + lr=lr, + beta_1=beta_1, + beta_2=beta_2, + epsilon=epsilon, + decay=decay + ), + metrics=[mae] ) - losses = [] - trained_epochs = epochs - if v_x is not None and v_y is not None: - best_model_params = self.state_dict() - best_valid_loss = None - valid_count = 0 - for e in range(epochs): - train_x, train_y = self._batch(l_x, l_y, batch_size) - valid_x, valid_y = self._batch(v_x, v_y, batch_size) - optimizer.zero_grad() - train_output = self(train_x) - train_loss = criterion(train_output, train_y) - train_loss.backward() - optimizer.step() - valid_output = self(valid_x) - valid_loss = criterion(valid_output, valid_y) - if v == 1: - logger.log('debug', 'Epoch {} | Training Loss {} | ' - 'Validation Loss {}'.format(e + 1, train_loss, - valid_loss), call_loc='MLP') - losses.append((train_loss.item(), valid_loss.item())) - if best_valid_loss is None or valid_loss < best_valid_loss: - best_model_params = self.state_dict() - best_valid_loss = valid_loss - valid_count = 0 - else: - valid_count += 1 - if valid_count >= patience: - trained_epochs = e + 1 - break - self.load_state_dict(best_model_params) - + history = self._model.fit( + l_x, + l_y, + validation_data=(v_x, v_y), + callbacks=[EarlyStopping( + monitor='val_loss', + patience=250, + verbose=v, + mode='min', + restore_best_weights=True + )], + epochs=epochs, + verbose=v, + batch_size=batch_size + ) + epochs = len(history.history['loss']) else: - for e in range(epochs): - train_x, train_y = self._batch(l_x, l_y, batch_size) - optimizer.zero_grad() - output = self(train_x) - loss = criterion(output, train_y) - if v == 1: - logger.log('debug', 'Epoch {} | Training Loss {}' - .format(e + 1, loss), call_loc='MLP') - losses.append((loss.item(), None)) - loss.backward() - optimizer.step() - - logger.log('debug', 'Training complete after {} epochs' - .format(trained_epochs), call_loc='MLP') - return losses + self._model.fit( + l_x, + l_y, + epochs=epochs, + verbose=v, + batch_size=batch_size + ) + + logger.log('debug', 'Training complete after {} epochs'.format(epochs), + call_loc='MLP') def use(self, x: array) -> array: '''Uses neural network to predict values for supplied data - Args: x (numpy.array): input data to predict for - Returns numpy.array: predictions ''' - return asarray(self(tensor(x).float()).tolist()) + with get_default_graph().as_default(): + return self._model.predict(x) def save(self, filename: str=None): - '''Saves neural network to specified file - + '''Saves neural network to .h5 file filename (str): if None, uses MultilayerPerceptron._filename; otherwise, saves to this file ''' if filename is None: filename = self._filename - check_ext(filename) - save({ - 'layers': self._layers, - 'state_dict': self.state_dict() - }, filename) + if H5_EXT.match(filename) is None: + raise ValueError( + 'Invalid filename/extension, must be `.h5`: {}'.format( + filename + ) + ) + self._model.save(filename) logger.log('debug', 'Model saved to {}'.format(filename), call_loc='MLP') def load(self, filename: str=None): - '''Loads neural network from specified file - + '''Loads neural network from .h5 file Args: - filename (str): if None, uses MultilayerPerceptron._filename; - otherwise, loads from this file + filename (str): path to .h5 model file ''' if filename is None: filename = self._filename - check_ext(filename) - file_contents = load(filename) - layers = file_contents['layers'] - self._layers = [] - for idx, l in enumerate(layers): - if idx == 0: - self.add_layer(l[1], l[2], l[0]) - else: - self.add_layer(l[1], l[2]) - self.load_state_dict(file_contents['state_dict']) + self._model = load_model(filename) logger.log('debug', 'Model loaded from {}'.format(filename), call_loc='MLP') - - def forward(self, x: tensor) -> tensor: - '''Used by torch.nn.Module to perform forward pass through neural - network - - Args: - x (torch.tensor): data to pass through neural network - - Returns: - tensor: final layer's output - ''' - - for idx, layer in enumerate(self._layers): - if layer[2] is None: - x = getattr(self, '_l{}'.format(idx))(x) - else: - x = layer[2](getattr(self, '_l{}'.format(idx))(x)) - return x - - @staticmethod - def _batch(x: array, y: array, batch_size: int) -> array: - '''Returns a batch with specified size given input/target data - - Args: - x (np.array): input data - y (np.array): target data, same # samples as input data - batch_size (int): size of the batch - - Returns: - tuple: (np.array, np.array), each array of size batch_size - ''' - - if batch_size >= len(x): - x_b = tensor(x).float() - y_b = tensor(y).float() - else: - idxs = choice(arange(len(x)), batch_size, replace=False) - x_b = tensor(x[idxs]).float() - y_b = tensor(y[idxs]).float() - return (x_b, y_b) diff --git a/setup.py b/setup.py index 9f18a90..3d53fc2 100644 --- a/setup.py +++ b/setup.py @@ -1,28 +1,4 @@ from setuptools import setup, find_packages -from platform import system, mac_ver - -install_requires = [ - 'torch', - 'alvadescpy==0.1.0', - 'colorlogging==0.3.5', - 'ecabc==2.2.3', - 'matplotlib==3.1.2', - 'numpy==1.16.4', - 'padelpy==0.1.6', - 'pyyaml==5.1.1', - 'scikit-learn==0.21.2' -] - -s = system() -if s == 'Windows': - dependency_links = ['https://download.pytorch.org/whl/cpu/torch-1.3.1%2Bcpu-cp37-cp37m-win_amd64.whl'] -elif s == 'Linux': - if mac_ver()[0] != '': - dependency_links = ['https://download.pytorch.org/whl/cpu/torch-1.3.1-cp37-none-macosx_10_7_x86_64.whl'] - else: - dependency_links = ['https://download.pytorch.org/whl/cpu/torch-1.3.1%2Bcpu-cp37-cp37m-linux_x86_64.whl'] -else: - raise OSError('Unsupported OS: {}'.format(s)) setup( name='ecnet', @@ -36,7 +12,17 @@ license='MIT', packages=find_packages(), python_requires='~=3.7', - install_requires=install_requires, - dependency_links=dependency_links, + install_requires=[ + 'alvadescpy==0.1.0', + 'colorlogging==0.3.5', + 'ecabc==2.2.3', + 'keras==2.3.1', + 'matplotlib==3.1.2', + 'numpy==1.16.4', + 'padelpy==0.1.6', + 'pyyaml==5.1.1', + 'scikit-learn==0.21.2', + 'tensorflow==1.15.0' + ], zip_safe=False ) From bc483f0c999a94b8583ae40235135d19fb0da7e3 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Sun, 5 Jan 2020 13:39:55 -0500 Subject: [PATCH 31/40] Batch size max = size of learning set --- ecnet/tasks/tuning.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ecnet/tasks/tuning.py b/ecnet/tasks/tuning.py index d742052..65e0a08 100644 --- a/ecnet/tasks/tuning.py +++ b/ecnet/tasks/tuning.py @@ -73,10 +73,10 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int, } value_ranges = [ - ('float', (1e-9, 1e-4)), # Learning rate decay - ('float', (1e-5, 0.1)), # Learning rate - ('int', (1, len(df))), # Batch size - ('int', (64, 1024)) # Patience + ('float', (1e-9, 1e-4)), # Learning rate decay + ('float', (1e-5, 0.1)), # Learning rate + ('int', (1, len(df.learn_set))), # Batch size + ('int', (64, 1024)) # Patience ] for _ in range(len(vars['hidden_layers'])): From 2510dc680befdd161d3baed1b46c4651dc1df8d7 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Sun, 5 Jan 2020 13:41:37 -0500 Subject: [PATCH 32/40] .ecnet -> .h5 for model files --- ecnet/server.py | 12 ++++++------ ecnet/tasks/training.py | 6 +++--- ecnet/utils/server_utils.py | 18 +++++++++--------- ecnet/workflows/workflow_utils.py | 4 ++-- tests/models/test_mlp.py | 2 +- tests/server/test_server.py | 8 ++++---- tests/utils/test_server_utils.py | 24 ++++++++++++------------ 7 files changed, 37 insertions(+), 37 deletions(-) diff --git a/ecnet/server.py b/ecnet/server.py index 478343e..216c2e0 100644 --- a/ecnet/server.py +++ b/ecnet/server.py @@ -187,7 +187,7 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int, def train(self, shuffle: str=None, split: list=None, retrain: bool=False, validate: bool=False, selection_set: str=None, - selection_fn: str='rmse', model_filename: str='model.ecnet', + selection_fn: str='rmse', model_filename: str='model.h5', verbose=0) -> list: '''Trains neural network(s) using currently-loaded data; single NN if no project is created, all candidates if created @@ -204,7 +204,7 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, set; `learn`, `valid`, `train`, `test`, None (all data) selection_fn (str): candidates are selected based on this error metric; `rmse`, `mean_abs_error`, `med_abs_error` - model_filename (str): if project not created, saves `.ecnet` file + model_filename (str): if project not created, saves `.h5` file here verbose (int): 1 to display loss at each epoch, 0 otherwise (single model only) @@ -247,7 +247,7 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, return None def use(self, dset: str=None, output_filename: str=None, - model_filename: str='model.ecnet') -> list: + model_filename: str='model.h5') -> list: '''Uses trained neural network(s) to predict for specified set; single NN if no project created, best pool candidates if created @@ -255,7 +255,7 @@ def use(self, dset: str=None, output_filename: str=None, dset (str): set to predict for; `learn`, `valid`, `train`, `test`, None (all sets) output_filename (str): if supplied, saves results to this CSV file - model_filename (str): if supplied, use specified .ecnet model file + model_filename (str): if supplied, use specified .h5 model file Returns: list: list of results for specified set @@ -278,7 +278,7 @@ def use(self, dset: str=None, output_filename: str=None, return results def errors(self, *args, dset: str=None, - model_filename: str='model.ecnet') -> dict: + model_filename: str='model.h5') -> dict: '''Obtains various errors for specified set Args: @@ -286,7 +286,7 @@ def errors(self, *args, dset: str=None, `med_abs_error`, `r2` dset (str): set to obtain errors for; `learn`, `valid`, `train`, `test`, None (all sets) - model_filename (str): if specified, uses .ecnet model file for error + model_filename (str): if specified, uses .h5 model file for error calculations Returns: diff --git a/ecnet/tasks/training.py b/ecnet/tasks/training.py index c09db26..0f7b702 100644 --- a/ecnet/tasks/training.py +++ b/ecnet/tasks/training.py @@ -72,7 +72,7 @@ def train_project(prj_name: str, num_pools: int, num_candidates: int, candidate, model=True ) - save_df(df, filename.replace('model.ecnet', 'data.d')) + save_df(df, filename.replace('model.h5', 'data.d')) if num_processes > 1: pool_errors[pool].append(train_pool.apply_async( @@ -108,6 +108,6 @@ def train_project(prj_name: str, num_pools: int, num_candidates: int, pool_fp = get_candidate_path(prj_name, p_idx, p_best=True) resave_model(candidate_fp, pool_fp) resave_df( - candidate_fp.replace('model.ecnet', 'data.d'), - pool_fp.replace('model.ecnet', 'data.d') + candidate_fp.replace('model.h5', 'data.d'), + pool_fp.replace('model.h5', 'data.d') ) diff --git a/ecnet/utils/server_utils.py b/ecnet/utils/server_utils.py index cc35981..f6d7e82 100644 --- a/ecnet/utils/server_utils.py +++ b/ecnet/utils/server_utils.py @@ -82,13 +82,13 @@ def default_config() -> dict: def get_candidate_path(prj: str, pool: int, candidate: int=None, model: bool=False, p_best: bool=False) -> str: - '''Get path to various states of model.ecnet files + '''Get path to various states of model.h5 files Args: prj (str): project name pool (int): pool number candidate (int): candidate number - model (bool): if True, appends `model.ecnet` to path + model (bool): if True, appends `model.h5` to path p_best (bool): if True, removes candidate folder from path Returns: @@ -100,13 +100,13 @@ def get_candidate_path(prj: str, pool: int, candidate: int=None, prj, 'pool_{}'.format(pool), 'candidate_{}'.format(candidate), - 'model.ecnet' + 'model.h5' ) elif p_best: return path.join( prj, 'pool_{}'.format(pool), - 'model.ecnet' + 'model.h5' ) else: return path.join( @@ -288,7 +288,7 @@ def resave_df(old: str, new: str): def resave_model(old: str, new: str): - '''Resaves .ecnet model file + '''Resaves .h5 model file Args: old (str): path to existing file @@ -367,7 +367,7 @@ def save_project(prj_name: str, filename: str, config_filename: str, def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, - retrain: bool=False, filename: str='model.ecnet', + retrain: bool=False, filename: str='model.h5', validate: bool=True, save: bool=True, verbose: int=0) -> tuple: '''Trains neural network @@ -379,7 +379,7 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, eval_fn (str): error function to evaluate the model; `rmse`, `mean_abs_error`, `med_abs_error` retrain (bool): if True, opens model for additional training - filename (str): path to .ecnet model file, loads from this if retraining + filename (str): path to .h5 model file, loads from this if retraining save (bool): if True, saves model to `filename` verbose (int): 1 to display loss at each epoch, 0 otherwise (single model only) @@ -438,14 +438,14 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, def use_model(sets: PackagedData, dset: str, - filename: str='model.ecnet') -> array: + filename: str='model.h5') -> array: '''Uses existing model to predict data Args: sets (ecnet.utils.data_utils.PackagedData): data sets dset (str): set to predict for; `learn`, `valid`, `train`, `test`, None (all sets) - filename (str): path to existing .ecnet model file + filename (str): path to existing .h5 model file Returns: numpy.array: predicted data diff --git a/ecnet/workflows/workflow_utils.py b/ecnet/workflows/workflow_utils.py index 5ccfb75..8d0c473 100644 --- a/ecnet/workflows/workflow_utils.py +++ b/ecnet/workflows/workflow_utils.py @@ -83,13 +83,13 @@ def find_optimal_num_inputs(db_name: str, eval_set: str, if num_processes > 1: errors.append([d_idx, train_pool.apply_async( train_model, [ - sets, conf, eval_set, 'med_abs_error', False, '_.ecnet', + sets, conf, eval_set, 'med_abs_error', False, '_.h5', False, False ] )]) else: errors.append([d_idx, train_model( - sets, conf, eval_set, 'med_abs_error', False, '_.ecnet', + sets, conf, eval_set, 'med_abs_error', False, '_.h5', False, False )[0]]) diff --git a/tests/models/test_mlp.py b/tests/models/test_mlp.py index 18766eb..51fd99f 100644 --- a/tests/models/test_mlp.py +++ b/tests/models/test_mlp.py @@ -27,7 +27,7 @@ def test_mlp(self): results = mlp_saved.use(array([[1, 1, 1], [0, 0, 0]])) self.assertAlmostEqual(results[0][0], 1, 3) self.assertAlmostEqual(results[1][0], 0, 3) - remove('model.ecnet') + remove('model.h5') if __name__ == '__main__': diff --git a/tests/server/test_server.py b/tests/server/test_server.py index 7730309..1653310 100644 --- a/tests/server/test_server.py +++ b/tests/server/test_server.py @@ -58,14 +58,14 @@ def test_train_project(self): self.assertTrue(exists(join( 'test_project', 'pool_{}'.format(pool), - 'model.ecnet' + 'model.h5' ))) for candidate in range(2): self.assertTrue(exists(join( 'test_project', 'pool_{}'.format(pool), 'candidate_{}'.format(candidate), - 'model.ecnet' + 'model.h5' ))) remove('config.yml') rmtree('test_project') @@ -109,14 +109,14 @@ def test_multiprocessing_train(self): self.assertTrue(exists(join( 'test_project', 'pool_{}'.format(pool), - 'model.ecnet' + 'model.h5' ))) for candidate in range(4): self.assertTrue(exists(join( 'test_project', 'pool_{}'.format(pool), 'candidate_{}'.format(candidate), - 'model.ecnet' + 'model.h5' ))) remove('config.yml') rmtree('test_project') diff --git a/tests/utils/test_server_utils.py b/tests/utils/test_server_utils.py index 52da43f..cbaa0fd 100644 --- a/tests/utils/test_server_utils.py +++ b/tests/utils/test_server_utils.py @@ -57,12 +57,12 @@ def test_get_candidate_path(self): ) self.assertEqual( server_utils.get_candidate_path(prj_name, pool, candidate, True), - join('test_project', 'pool_1', 'candidate_2', 'model.ecnet') + join('test_project', 'pool_1', 'candidate_2', 'model.h5') ) self.assertEqual( server_utils.get_candidate_path(prj_name, pool, candidate, p_best=True), - join('test_project', 'pool_1', 'model.ecnet') + join('test_project', 'pool_1', 'model.h5') ) def test_get_error(self): @@ -165,10 +165,10 @@ def test_train_model(self): config = server_utils.default_config() config['epochs'] = 100 _ = server_utils.train_model( - pd, config, 'test', 'r2', filename='test_train.ecnet' + pd, config, 'test', 'r2', filename='test_train.h5' ) - self.assertTrue(exists('test_train.ecnet')) - remove('test_train.ecnet') + self.assertTrue(exists('test_train.h5')) + remove('test_train.h5') def test_use_model(self): @@ -179,29 +179,29 @@ def test_use_model(self): config = server_utils.default_config() config['epochs'] = 100 _ = server_utils.train_model( - pd, config, 'test', 'rmse', filename='test_use.ecnet' + pd, config, 'test', 'rmse', filename='test_use.h5' ) self.assertEqual( - len(server_utils.use_model(pd, 'learn', 'test_use.ecnet')), + len(server_utils.use_model(pd, 'learn', 'test_use.h5')), len(pd.learn_y) ) self.assertEqual( - len(server_utils.use_model(pd, 'valid', 'test_use.ecnet')), + len(server_utils.use_model(pd, 'valid', 'test_use.h5')), len(pd.valid_y) ) self.assertEqual( - len(server_utils.use_model(pd, 'test', 'test_use.ecnet')), + len(server_utils.use_model(pd, 'test', 'test_use.h5')), len(pd.test_y) ) self.assertEqual( - len(server_utils.use_model(pd, 'train', 'test_use.ecnet')), + len(server_utils.use_model(pd, 'train', 'test_use.h5')), len(pd.learn_y) + len(pd.valid_y) ) self.assertEqual( - len(server_utils.use_model(pd, None, 'test_use.ecnet')), + len(server_utils.use_model(pd, None, 'test_use.h5')), len(pd.learn_y) + len(pd.valid_y) + len(pd.test_y) ) - remove('test_use.ecnet') + remove('test_use.h5') if __name__ == '__main__': From f1ac071368bd6a66b01daac4ff24f8f7b6a6571d Mon Sep 17 00:00:00 2001 From: tjkessler Date: Sun, 5 Jan 2020 13:51:35 -0500 Subject: [PATCH 33/40] Add patience hyperparam as arg to TF mlp.fit method --- ecnet/models/mlp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ecnet/models/mlp.py b/ecnet/models/mlp.py index 39c3a90..0e982af 100644 --- a/ecnet/models/mlp.py +++ b/ecnet/models/mlp.py @@ -75,7 +75,7 @@ def add_layer(self, num_neurons: int, activation: str, def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None, epochs: int=1500, lr: float=0.001, beta_1: float=0.9, beta_2: float=0.999, epsilon: float=0.0000001, decay: float=0.0, - v: int=0, batch_size: int=32): + v: int=0, batch_size: int=32, patience: int=128): '''Fits neural network to supplied inputs and targets Args: l_x (numpy.array): learning input data @@ -114,7 +114,7 @@ def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None, validation_data=(v_x, v_y), callbacks=[EarlyStopping( monitor='val_loss', - patience=250, + patience=patience, verbose=v, mode='min', restore_best_weights=True From 5b30b15fe124e462a75148bcbaecb5825765e5e8 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Sun, 5 Jan 2020 14:00:07 -0500 Subject: [PATCH 34/40] MultilayerPerceptron.fit returns (learn losses, valid losses) --- ecnet/models/mlp.py | 22 +++++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/ecnet/models/mlp.py b/ecnet/models/mlp.py index 0e982af..1aab49c 100644 --- a/ecnet/models/mlp.py +++ b/ecnet/models/mlp.py @@ -41,6 +41,7 @@ class MultilayerPerceptron: def __init__(self, filename: str='model.h5'): '''MultilayerPerceptron object: fits neural network to supplied inputs and targets + Args: filename (str): path to model save location (.h5 extension) ''' @@ -58,6 +59,7 @@ def __init__(self, filename: str='model.h5'): def add_layer(self, num_neurons: int, activation: str, input_dim: int=None): '''Adds a fully-connected layer to the model + Args: num_neurons (int): number of neurons for the layer activation (str): activation function for the layer (see Keras @@ -75,8 +77,9 @@ def add_layer(self, num_neurons: int, activation: str, def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None, epochs: int=1500, lr: float=0.001, beta_1: float=0.9, beta_2: float=0.999, epsilon: float=0.0000001, decay: float=0.0, - v: int=0, batch_size: int=32, patience: int=128): + v: int=0, batch_size: int=32, patience: int=128) -> tuple: '''Fits neural network to supplied inputs and targets + Args: l_x (numpy.array): learning input data l_y (numpy.array): learning target data @@ -93,6 +96,11 @@ def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None, decay (float): learning rate decay for Adam optimizer v (int): verbose training, `0` for no printing, `1` for printing batch_size (int): number of learning samples per batch + + Returns: + tuple: (learn losses iterable, valid losses iterable); both + iterables are the same size; if not validating, valid losses + iterable populated with `None` ''' self._model.compile( @@ -123,24 +131,31 @@ def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None, verbose=v, batch_size=batch_size ) - epochs = len(history.history['loss']) + learn_loss = history.history['loss'] + valid_loss = history.history['val_loss'] + epochs = len(learn_loss) else: - self._model.fit( + history = self._model.fit( l_x, l_y, epochs=epochs, verbose=v, batch_size=batch_size ) + learn_loss = history.history['loss'] + valid_loss = [None for _ in range(len(learn_loss))] logger.log('debug', 'Training complete after {} epochs'.format(epochs), call_loc='MLP') + return (learn_loss, valid_loss) def use(self, x: array) -> array: '''Uses neural network to predict values for supplied data + Args: x (numpy.array): input data to predict for Returns + numpy.array: predictions ''' @@ -167,6 +182,7 @@ def save(self, filename: str=None): def load(self, filename: str=None): '''Loads neural network from .h5 file + Args: filename (str): path to .h5 model file ''' From e5418139f70900f89aad9aeadf064245580476d7 Mon Sep 17 00:00:00 2001 From: Travis Kessler Date: Sun, 5 Jan 2020 14:59:34 -0500 Subject: [PATCH 35/40] TF installation optional --- setup.py | 31 +++++++++++++++++++------------ 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/setup.py b/setup.py index 3d53fc2..a869f5f 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,22 @@ from setuptools import setup, find_packages +from sys import argv + +install_requires = [ + 'alvadescpy==0.1.0', + 'colorlogging==0.3.5', + 'ecabc==2.2.3', + 'keras==2.3.1', + 'matplotlib==3.1.2', + 'numpy==1.16.4', + 'padelpy==0.1.6', + 'pyyaml==5.1.1', + 'scikit-learn==0.21.2' +] + +if '--omit_tf' in argv: + argv.remove('--omit_tf') +else: + install_requires.append('tensorflow==1.15.0') setup( name='ecnet', @@ -12,17 +30,6 @@ license='MIT', packages=find_packages(), python_requires='~=3.7', - install_requires=[ - 'alvadescpy==0.1.0', - 'colorlogging==0.3.5', - 'ecabc==2.2.3', - 'keras==2.3.1', - 'matplotlib==3.1.2', - 'numpy==1.16.4', - 'padelpy==0.1.6', - 'pyyaml==5.1.1', - 'scikit-learn==0.21.2', - 'tensorflow==1.15.0' - ], + install_requires=install_requires, zip_safe=False ) From 6f4d7892dbd31b2d5193407e213f6376c5edb432 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Mon, 6 Jan 2020 13:49:52 -0500 Subject: [PATCH 36/40] TF: 1.X -> 2.0.0; removed Keras dependency --- setup.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/setup.py b/setup.py index a869f5f..540a1b9 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,6 @@ 'alvadescpy==0.1.0', 'colorlogging==0.3.5', 'ecabc==2.2.3', - 'keras==2.3.1', 'matplotlib==3.1.2', 'numpy==1.16.4', 'padelpy==0.1.6', @@ -16,7 +15,7 @@ if '--omit_tf' in argv: argv.remove('--omit_tf') else: - install_requires.append('tensorflow==1.15.0') + install_requires.append('tensorflow==2.0.0') setup( name='ecnet', From 9c46987356e055b77ba559e2d1212758871c02dc Mon Sep 17 00:00:00 2001 From: tjkessler Date: Mon, 6 Jan 2020 14:11:58 -0500 Subject: [PATCH 37/40] Updated to TF 2.0, lower-level implementation using tf.keras.models.Model parent --- ecnet/models/mlp.py | 276 ++++++++++++++++++++++++-------------------- 1 file changed, 148 insertions(+), 128 deletions(-) diff --git a/ecnet/models/mlp.py b/ecnet/models/mlp.py index 1aab49c..785403e 100644 --- a/ecnet/models/mlp.py +++ b/ecnet/models/mlp.py @@ -8,187 +8,207 @@ # Contains the "MultilayerPerceptron" (feed-forward neural network) class # -# Stdlib imports +from os import environ from re import compile, IGNORECASE -from os import devnull, environ -import sys - -# 3rd party imports -from tensorflow import get_default_graph, logging -from numpy import array -stderr = sys.stderr -sys.stderr = open(devnull, 'w') -from keras.backend import clear_session, reset_uids -from keras.callbacks import EarlyStopping -from keras.layers import Dense -from keras.losses import mean_squared_error -from keras.metrics import mae -from keras.models import load_model, Sequential -from keras.optimizers import Adam -sys.stderr = stderr - -# ECNet imports -from ecnet.utils.logging import logger -environ['TF_CPP_MIN_LOG_LEVEL'] = '2' -logging.set_verbosity(logging.ERROR) +from h5py import File +from numpy import array, string_, zeros + +from tensorflow import config, Tensor +from tensorflow.keras.callbacks import EarlyStopping +from tensorflow.keras.layers import Dense +from tensorflow.keras.losses import MeanSquaredError +from tensorflow.keras.models import Model +from tensorflow.keras.optimizers import Adam + +from ecnet.utils.logging import logger +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' +config.experimental_run_functions_eagerly(True) H5_EXT = compile(r'.*\.h5', flags=IGNORECASE) -class MultilayerPerceptron: +def check_h5(filename: str): + ''' Ensures a given filename has an `.h5` extension + + Args: + filename (str): filename to check + ''' + + if H5_EXT.match(filename) is None: + raise ValueError( + 'Invalid filename/extension, must be `.h5`: {}'.format( + filename + ) + ) - def __init__(self, filename: str='model.h5'): - '''MultilayerPerceptron object: fits neural network to supplied inputs - and targets + +class MultilayerPerceptron(Model): + + def __init__(self, filename: str = 'model.h5'): + ''' MultilayerPerceptron: Feed-forward neural network; variable number + of layers, variable size/activations of layers; handles training, + saving/loading of models Args: - filename (str): path to model save location (.h5 extension) + filename (str): filename/path for the model (default: `model.h5`) ''' - if H5_EXT.match(filename) is None: - raise ValueError( - 'Invalid filename/extension, must be `.h5`: {}'.format( - filename - ) - ) + super(MultilayerPerceptron, self).__init__() + check_h5(filename) self._filename = filename - clear_session() - self._model = Sequential(name=filename.lower().replace('.h5', '')) + self._layers = [] def add_layer(self, num_neurons: int, activation: str, - input_dim: int=None): - '''Adds a fully-connected layer to the model + input_dim: int = None): + ''' add_layer: adds a layer to the MLP; layers are added sequentially; + first layer must have input dimensionality specified Args: - num_neurons (int): number of neurons for the layer - activation (str): activation function for the layer (see Keras - activation function documentation) - input_dim (int): if not None (input layer), specifies input - dimensionality + num_neurons (int): number of neurons in the layer + activation (str): activation function used by the layer; refer to + https://www.tensorflow.org/api_docs/python/tf/keras/activations + for usable activation functions + input_dim (int): specify input dimensionality for the first layer; + all other layers depend on previous layers' size (number of + neurons), should be kept as `None` (default value) ''' - self._model.add(Dense( + if len(self._layers) == 0: + if input_dim is None: + raise ValueError('First layer must have input_dim specified') + + self._layers.append(Dense( units=num_neurons, activation=activation, input_shape=(input_dim,) )) - def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None, - epochs: int=1500, lr: float=0.001, beta_1: float=0.9, - beta_2: float=0.999, epsilon: float=0.0000001, decay: float=0.0, - v: int=0, batch_size: int=32, patience: int=128) -> tuple: - '''Fits neural network to supplied inputs and targets + def call(self, x: Tensor) -> Tensor: + ''' call: used by Model.fit (parent) to perform feed-forward operations Args: - l_x (numpy.array): learning input data - l_y (numpy.array): learning target data - v_x (numpy.array): if not None, periodic validation is performed w/ - these inputs - v_y (numpy.array): if not None, periodic validation is performed w/ - these targets - epochs (int): number of learning epochs if not validating, maximum - number of learning epochs if performing periodic validation - lr (float): learning rate for Adam optimizer - beta_1 (float): beta_1 value for Adam optimizer - beta_2 (float): beta_2 value for Adam optimizer - epsilon (float): epsilon value for Adam optimizer - decay (float): learning rate decay for Adam optimizer - v (int): verbose training, `0` for no printing, `1` for printing - batch_size (int): number of learning samples per batch + x (tf.Tensor): data fed into first layer Returns: - tuple: (learn losses iterable, valid losses iterable); both - iterables are the same size; if not validating, valid losses - iterable populated with `None` + tf.Tensor: data resulting from last layer ''' - self._model.compile( - loss=mean_squared_error, - optimizer=Adam( - lr=lr, - beta_1=beta_1, - beta_2=beta_2, - epsilon=epsilon, - decay=decay - ), - metrics=[mae] - ) + for layer in self._layers: + x = layer(x) + return x + + def fit(self, l_x: array, l_y: array, v_x: array = None, v_y: array = None, + epochs: int = 1500, lr: float = 0.001, beta_1: float = 0.9, + beta_2: float = 0.999, epsilon: float = 0.0000001, + decay: float = 0.0, v: int = 0, batch_size: int = 32, + patience: int = 128) -> tuple: + ''' fit: trains model using supplied data; may supply additional data + to use as validation set (determines learning cutoff); hyperparameters + for Adam optimization function, batch size, patience (if validating) + may be specified + + Args: + l_x (np.array): learning input data; each sub-iterable is a sample + l_y (np.array): learning target data; each sub-iterable is a sample + v_x (np.array): validation input data (`None` for no validation) + v_y (np.array): validation target data (`None` for no validation) + epochs (int): number of training iterations, max iterations if + performing validation + lr (float): learning rate of Adam optimization fn + beta_1 (float): first moment estimate of Adam optimization fn + beta_2 (float): second moment estimate of Adam optimization fn + epsilon (float): number to prevent division by zero in Adam fn + decay (float): decay of learning date in Adam optimization fn + v (int): whether Model.fit (parent) is verbose (1 True, 0 False) + batch_size (int): size of each training batch + patience (int): maximum number of epochs to wait before better + validation loss found; if not found, training terminates, best + weights restored + + Returns: + tuple: (list: learn losses, list: valid losses); each list equal + length; each list element represents loss at corresponding + epoch + ''' + + self.compile(optimizer=Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, + epsilon=epsilon, + decay=decay), + loss=MeanSquaredError()) if v_x is not None and v_y is not None: - history = self._model.fit( - l_x, - l_y, - validation_data=(v_x, v_y), - callbacks=[EarlyStopping( - monitor='val_loss', - patience=patience, - verbose=v, - mode='min', - restore_best_weights=True - )], - epochs=epochs, - verbose=v, - batch_size=batch_size - ) - learn_loss = history.history['loss'] - valid_loss = history.history['val_loss'] - epochs = len(learn_loss) + + callback = EarlyStopping(monitor='val_loss', patience=patience, + restore_best_weights=True) + history = super().fit(l_x, l_y, batch_size=batch_size, + epochs=epochs, verbose=v, + callbacks=[callback], + validation_data=(v_x, v_y)) + return (history.history['loss'], history.history['val_loss']) + else: - history = self._model.fit( - l_x, - l_y, - epochs=epochs, - verbose=v, - batch_size=batch_size - ) - learn_loss = history.history['loss'] - valid_loss = [None for _ in range(len(learn_loss))] - logger.log('debug', 'Training complete after {} epochs'.format(epochs), - call_loc='MLP') - return (learn_loss, valid_loss) + history = super().fit(l_x, l_y, batch_size=batch_size, + epochs=epochs, verbose=v) + return (history.history['loss'], [None for _ in range(epochs)]) def use(self, x: array) -> array: - '''Uses neural network to predict values for supplied data + ''' use: uses the model to predict values for supplied data Args: - x (numpy.array): input data to predict for - Returns + x (np.array): input data to predict for - numpy.array: predictions + Returns: + np.array: predicted values ''' - with get_default_graph().as_default(): - return self._model.predict(x) + return self.predict(x) - def save(self, filename: str=None): - '''Saves neural network to .h5 file - filename (str): if None, uses MultilayerPerceptron._filename; - otherwise, saves to this file + def save(self, filename: str = None): + ''' save: saves the model weights, architecture to either the filename/ + path specified when object was created, or new, supplied filename/path + + Args: + filename (str): new filepath if different than init filename/path ''' if filename is None: filename = self._filename - if H5_EXT.match(filename) is None: - raise ValueError( - 'Invalid filename/extension, must be `.h5`: {}'.format( - filename - ) - ) - self._model.save(filename) + check_h5(filename) + self.save_weights(filename, save_format='h5') + input_size = self.layers[0].get_config()['batch_input_shape'][1] + layer_sizes = [l.get_config()['units'] for l in self.layers] + layer_activ = [l.get_config()['activation'] for l in self.layers] + with File(filename, 'a') as hf: + hf['mlp_input_size'] = input_size + hf['mlp_layer_sizes'] = layer_sizes + hf['mlp_layer_activ'] = string_(layer_activ) + hf.close() logger.log('debug', 'Model saved to {}'.format(filename), call_loc='MLP') - def load(self, filename: str=None): - '''Loads neural network from .h5 file + def load(self, filename: str = None): + ''' load: loads a saved model, restoring the architecture/weights; + loads from filename/path specified during object initialization, + unless new filename/path specified Args: - filename (str): path to .h5 model file + filename (str): new filepath if different than init filename/path ''' if filename is None: filename = self._filename - self._model = load_model(filename) + with File(filename, 'r') as hf: + input_size = hf.get('mlp_input_size').value + layer_sizes = hf.get('mlp_layer_sizes').value + layer_activ = hf.get('mlp_layer_activ').value + hf.close() + self.add_layer(layer_sizes[0], layer_activ[0].decode('ascii'), + input_size) + for idx, layer in enumerate(layer_sizes[1:]): + self.add_layer(layer, layer_activ[idx].decode('ascii')) + self.build(input_shape=(None, input_size)) + self.load_weights(filename) logger.log('debug', 'Model loaded from {}'.format(filename), call_loc='MLP') From 8121e81f87b020728da71229c4b7f8040b1a4881 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Mon, 6 Jan 2020 14:15:10 -0500 Subject: [PATCH 38/40] Bug fix --- ecnet/models/mlp.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ecnet/models/mlp.py b/ecnet/models/mlp.py index 785403e..863f897 100644 --- a/ecnet/models/mlp.py +++ b/ecnet/models/mlp.py @@ -23,7 +23,7 @@ from ecnet.utils.logging import logger -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' +environ['TF_CPP_MIN_LOG_LEVEL'] = '2' config.experimental_run_functions_eagerly(True) H5_EXT = compile(r'.*\.h5', flags=IGNORECASE) From b37c1e565eb8906cde252607f1c1f2f824f875fd Mon Sep 17 00:00:00 2001 From: tjkessler Date: Mon, 6 Jan 2020 14:36:59 -0500 Subject: [PATCH 39/40] lr ^, epochs \/ --- tests/models/test_mlp.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/models/test_mlp.py b/tests/models/test_mlp.py index 51fd99f..f3420c1 100644 --- a/tests/models/test_mlp.py +++ b/tests/models/test_mlp.py @@ -15,7 +15,8 @@ def test_mlp(self): mlp.add_layer(9, 'relu', 3) mlp.add_layer(6, 'relu') mlp.add_layer(1, 'linear') - mlp.fit(array([[1, 1, 1], [0, 0, 0]]), array([[1], [0]]), epochs=20000) + mlp.fit(array([[1, 1, 1], [0, 0, 0]]), array([[1], [0]]), epochs=2000, + lr=0.01) results = mlp.use(array([[1, 1, 1], [0, 0, 0]])) self.assertAlmostEqual(results[0][0], 1, 3) From 7fcfcee7edbcd1c74350b7edb5a8dfd3dcf7a3d7 Mon Sep 17 00:00:00 2001 From: tjkessler Date: Mon, 6 Jan 2020 16:13:55 -0500 Subject: [PATCH 40/40] Version bump: 3.2.3 -> 3.3.0 --- ecnet/models/mlp.py | 8 +++-- ecnet/server.py | 50 +++++++++++++++---------------- ecnet/tasks/limit_inputs.py | 8 ++--- ecnet/tasks/training.py | 4 +-- ecnet/tasks/tuning.py | 12 ++++---- ecnet/tools/database.py | 12 ++++---- ecnet/tools/plotting.py | 17 ++++++----- ecnet/tools/project.py | 8 ++--- ecnet/utils/data_utils.py | 18 +++++------ ecnet/utils/error_utils.py | 4 +-- ecnet/utils/logging.py | 4 +-- ecnet/utils/server_utils.py | 17 ++++++----- ecnet/workflows/ecrl_workflow.py | 36 ++++++++++++++-------- ecnet/workflows/workflow_utils.py | 18 +++++++++-- 14 files changed, 122 insertions(+), 94 deletions(-) diff --git a/ecnet/models/mlp.py b/ecnet/models/mlp.py index 863f897..2755e99 100644 --- a/ecnet/models/mlp.py +++ b/ecnet/models/mlp.py @@ -2,18 +2,19 @@ # -*- coding: utf-8 -*- # # ecnet/models/mlp.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains the "MultilayerPerceptron" (feed-forward neural network) class # +# Stdlib imports from os import environ from re import compile, IGNORECASE +# 3rd party imports from h5py import File from numpy import array, string_, zeros - from tensorflow import config, Tensor from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras.layers import Dense @@ -21,6 +22,7 @@ from tensorflow.keras.models import Model from tensorflow.keras.optimizers import Adam +# ECNet imports from ecnet.utils.logging import logger environ['TF_CPP_MIN_LOG_LEVEL'] = '2' diff --git a/ecnet/server.py b/ecnet/server.py index 216c2e0..c72c1b0 100644 --- a/ecnet/server.py +++ b/ecnet/server.py @@ -2,8 +2,8 @@ # -*- coding: utf-8 -*- # # ecnet/server.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains the "Server" class, which handles ECNet project creation, neural # network model creation, data hand-off to models, prediction error @@ -26,8 +26,8 @@ class Server: - def __init__(self, model_config: str='config.yml', prj_file: str=None, - num_processes: int=1): + def __init__(self, model_config: str = 'config.yml', prj_file: str = None, + num_processes: int = 1): '''Server object: handles data loading, model creation, data-to-model hand-off, data input parameter selection, hyperparameter tuning @@ -69,8 +69,8 @@ def __init__(self, model_config: str='config.yml', prj_file: str=None, self._vars = default_config() save_config(self._vars, self._cf_file) - def load_data(self, filename: str, random: bool=False, split: list=None, - normalize: bool=False): + def load_data(self, filename: str, random: bool = False, + split: list = None, normalize: bool = False): '''Loads data from an ECNet-formatted CSV database Args: @@ -90,8 +90,8 @@ def load_data(self, filename: str, random: bool=False, split: list=None, self._df.create_sets(random, split) self._sets = self._df.package_sets() - def create_project(self, project_name: str, num_pools: int=1, - num_candidates: int=1): + def create_project(self, project_name: str, num_pools: int = 1, + num_candidates: int = 1): '''Creates folder hierarchy for a new project Args: @@ -111,8 +111,8 @@ def create_project(self, project_name: str, num_pools: int=1, logger.log('debug', 'Number of candidates/pool: {}'.format( num_candidates), call_loc='PROJECT') - def limit_inputs(self, limit_num: int, num_estimators: int=None, - eval_set: str='learn', output_filename: str=None, + def limit_inputs(self, limit_num: int, num_estimators: int = None, + eval_set: str = 'learn', output_filename: str = None, **kwargs) -> list: '''Selects `limit_num` influential input parameters using random forest regression @@ -149,9 +149,9 @@ def limit_inputs(self, limit_num: int, num_estimators: int=None, return result def tune_hyperparameters(self, num_employers: int, num_iterations: int, - shuffle: bool=None, split: list=None, - validate: bool=True, eval_set: str=None, - eval_fn: str='rmse', epochs: int=300): + shuffle: bool = None, split: list = None, + validate: bool = True, eval_set: str = None, + eval_fn: str = 'rmse', epochs: int = 300): '''Tunes neural network learning hyperparameters using an artificial bee colony algorithm; tuned hyperparameters are saved to Server's model configuration file @@ -185,10 +185,10 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int, ) save_config(self._vars, self._cf_file) - def train(self, shuffle: str=None, split: list=None, retrain: bool=False, - validate: bool=False, selection_set: str=None, - selection_fn: str='rmse', model_filename: str='model.h5', - verbose=0) -> list: + def train(self, shuffle: str = None, split: list = None, + retrain: bool = False, validate: bool = False, + selection_set: str = None, selection_fn: str = 'rmse', + model_filename: str = 'model.h5', verbose: int = 0) -> tuple: '''Trains neural network(s) using currently-loaded data; single NN if no project is created, all candidates if created @@ -210,8 +210,8 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, model only) Returns: - list: if training single model, returns list of learn/valid losses, - else None + tuple: if training single model, returns tuple of learn/valid + losses, else None ''' if self._prj_name is None: @@ -246,8 +246,8 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False, ) return None - def use(self, dset: str=None, output_filename: str=None, - model_filename: str='model.h5') -> list: + def use(self, dset: str = None, output_filename: str = None, + model_filename: str = 'model.h5') -> list: '''Uses trained neural network(s) to predict for specified set; single NN if no project created, best pool candidates if created @@ -277,8 +277,8 @@ def use(self, dset: str=None, output_filename: str=None, call_loc='USE') return results - def errors(self, *args, dset: str=None, - model_filename: str='model.h5') -> dict: + def errors(self, *args, dset: str = None, + model_filename: str = 'model.h5') -> dict: '''Obtains various errors for specified set Args: @@ -304,8 +304,8 @@ def errors(self, *args, dset: str=None, logger.log('debug', 'Errors: {}'.format(errors), call_loc='ERRORS') return errors - def save_project(self, filename: str=None, clean_up: bool=True, - del_candidates: bool=False): + def save_project(self, filename: str = None, clean_up: bool = True, + del_candidates: bool = False): '''Saves current state of project to a .prj file Args: diff --git a/ecnet/tasks/limit_inputs.py b/ecnet/tasks/limit_inputs.py index 58fa8ae..48b1d74 100644 --- a/ecnet/tasks/limit_inputs.py +++ b/ecnet/tasks/limit_inputs.py @@ -2,8 +2,8 @@ # -*- coding: utf-8 -*- # # ecnet/tasks/limit_inputs.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains functions for selecting influential input parameters # @@ -21,8 +21,8 @@ from ecnet.utils.server_utils import get_x, get_y -def limit_rforest(df: DataFrame, limit_num: int, num_estimators: int=None, - num_processes: int=1, eval_set: str='learn', +def limit_rforest(df: DataFrame, limit_num: int, num_estimators: int = None, + num_processes: int = 1, eval_set: str = 'learn', **kwargs) -> list: '''Uses random forest regression to select input parameters diff --git a/ecnet/tasks/training.py b/ecnet/tasks/training.py index 0f7b702..252b14f 100644 --- a/ecnet/tasks/training.py +++ b/ecnet/tasks/training.py @@ -2,8 +2,8 @@ # -*- coding: utf-8 -*- # # ecnet/tasks/training.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains function for project training (multiprocessed training) # diff --git a/ecnet/tasks/tuning.py b/ecnet/tasks/tuning.py index 65e0a08..3792a6d 100644 --- a/ecnet/tasks/tuning.py +++ b/ecnet/tasks/tuning.py @@ -2,8 +2,8 @@ # -*- coding: utf-8 -*- # # ecnet/tasks/tuning.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains functions/fitness functions for tuning hyperparameters # @@ -22,10 +22,10 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int, - num_iterations: int, num_processes: int=1, - shuffle: str=None, split: list=None, - validate: bool=True, eval_set: str=None, - eval_fn: str='rmse', epochs: int=300) -> dict: + num_iterations: int, num_processes: int = 1, + shuffle: str = None, split: list = None, + validate: bool = True, eval_set: str = None, + eval_fn: str = 'rmse', epochs: int = 300) -> dict: '''Tunes neural network learning/architecture hyperparameters Args: diff --git a/ecnet/tools/database.py b/ecnet/tools/database.py index 5385481..4be7e76 100644 --- a/ecnet/tools/database.py +++ b/ecnet/tools/database.py @@ -2,8 +2,8 @@ # -*- coding: utf-8 -*- # # ecnet/tools/database.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains functions for creating ECNet-formatted databases # @@ -20,7 +20,7 @@ try: import pybel -except: +except ImportError: pybel = None @@ -35,9 +35,9 @@ def __init__(self, id): self.inputs = None -def create_db(smiles: list, db_name: str, targets: list=None, - id_prefix: str='', extra_strings: dict={}, backend: str='padel', - convert_mdl: bool=False): +def create_db(smiles: list, db_name: str, targets: list = None, + id_prefix: str = '', extra_strings: dict = {}, + backend: str = 'padel', convert_mdl: bool = False): ''' create_db: creates an ECNet-formatted database from SMILES strings using either PaDEL-Descriptor or alvaDesc software; using alvaDesc requires a valid installation/license of alvaDesc diff --git a/ecnet/tools/plotting.py b/ecnet/tools/plotting.py index 441db81..895aafb 100644 --- a/ecnet/tools/plotting.py +++ b/ecnet/tools/plotting.py @@ -2,8 +2,8 @@ # -*- coding: utf-8 -*- # # ecnet/tools/plotting.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains functions/classes for creating various plots # @@ -18,9 +18,10 @@ class ParityPlot: - def __init__(self, title: str='Parity Plot', - x_label: str='Experimental Value', - y_label: str='Predicted Value', font: str='Times New Roman'): + def __init__(self, title: str = 'Parity Plot', + x_label: str = 'Experimental Value', + y_label: str = 'Predicted Value', + font: str = 'Times New Roman'): ''' ParityPlot: creates a plot of predicted values vs. experimental data relative to a 1:1 parity line @@ -39,7 +40,7 @@ def __init__(self, title: str='Parity Plot', self._min_val = 0 self._labels = None - def add_series(self, x_vals, y_vals, name: str=None, color: str=None): + def add_series(self, x_vals, y_vals, name: str = None, color: str = None): ''' Adds data to the plot Args: @@ -67,7 +68,7 @@ def add_series(self, x_vals, y_vals, name: str=None, color: str=None): if y_min < self._min_val: self._min_val = y_min - def add_error_bars(self, error: float, label: str=None): + def add_error_bars(self, error: float, label: str = None): ''' Adds error bars, +/- the error relative to the 1:1 parity line Args: @@ -96,7 +97,7 @@ def save(self, filename: str): self._add_parity_line() plt.savefig(filename) - def _add_parity_line(self, offset: float=0.0): + def _add_parity_line(self, offset: float = 0.0): ''' Adds a 1:1 parity line Args: diff --git a/ecnet/tools/project.py b/ecnet/tools/project.py index b09a7d2..ba00b57 100644 --- a/ecnet/tools/project.py +++ b/ecnet/tools/project.py @@ -2,8 +2,8 @@ # -*- coding: utf-8 -*- # # ecnet/tools/project.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains functions for predicting data using pre-existing .prj files # @@ -20,8 +20,8 @@ from ecnet.tools.database import create_db -def predict(smiles: list, prj_file: str, results_file: str=None, - backend: str='padel') -> list: +def predict(smiles: list, prj_file: str, results_file: str = None, + backend: str = 'padel') -> list: ''' predict: predicts values for supplied molecules (SMILES strings) using pre-existing ECNet project (.prj) file diff --git a/ecnet/utils/data_utils.py b/ecnet/utils/data_utils.py index cf4e29d..0a32407 100644 --- a/ecnet/utils/data_utils.py +++ b/ecnet/utils/data_utils.py @@ -2,8 +2,8 @@ # -*- coding: utf-8 -*- # # ecnet/utils/data_utils.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains functions/classes for loading data, saving data, saving results # @@ -33,8 +33,8 @@ def __init__(self): class PackagedData: def __init__(self): - '''PackagedData object: contains lists of input and target data for data - set assignments + '''PackagedData object: contains lists of input and target data for + data set assignments ''' self.learn_x = [] @@ -112,7 +112,7 @@ def __len__(self): return len(self.data_points) - def create_sets(self, random: bool=False, split: list=[0.7, 0.2, 0.1]): + def create_sets(self, random: bool = False, split: list = [0.7, 0.2, 0.1]): '''Creates learning, validation and test sets Args: @@ -167,9 +167,9 @@ def create_sets(self, random: bool=False, split: list=[0.7, 0.2, 0.1]): logger.log('debug', 'Number of entries in test set: {}'.format( len(self.test_set)), call_loc='DF') - def create_sorted_sets(self, sort_str: str, split: list=[0.7, 0.2, 0.1]): - '''Creates random learn, validate and test sets, ensuring data points with - the supplied sort string are split proportionally between the sets + def create_sorted_sets(self, sort_str: str, split: list = [0.7, 0.2, 0.1]): + '''Creates random learn, validate and test sets, ensuring data points + with the supplied sort string are split proportionally between the sets Args: sort_str (str): database STRING value used to sort data points @@ -239,7 +239,7 @@ def normalize(self): (float(getattr(pt, inp)) - v_min) / (v_max - v_min) ) - def shuffle(self, sets: str='all', split: list=[0.7, 0.2, 0.1]): + def shuffle(self, sets: str = 'all', split: list = [0.7, 0.2, 0.1]): '''Shuffles learning, validation and test sets or learning and validation sets diff --git a/ecnet/utils/error_utils.py b/ecnet/utils/error_utils.py index 4546c39..16cbf1b 100644 --- a/ecnet/utils/error_utils.py +++ b/ecnet/utils/error_utils.py @@ -2,8 +2,8 @@ # -*- coding: utf-8 -*- # # ecnet/utils/error_utils.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains functions for error calculations # diff --git a/ecnet/utils/logging.py b/ecnet/utils/logging.py index f4cc13d..32798bc 100644 --- a/ecnet/utils/logging.py +++ b/ecnet/utils/logging.py @@ -2,8 +2,8 @@ # -*- coding: utf-8 -*- # # ecnet/utils/logging.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains logger used by ECNet # diff --git a/ecnet/utils/server_utils.py b/ecnet/utils/server_utils.py index f6d7e82..4d2b1f7 100644 --- a/ecnet/utils/server_utils.py +++ b/ecnet/utils/server_utils.py @@ -2,8 +2,8 @@ # -*- coding: utf-8 -*- # # ecnet/utils/server_utils.py -# v.3.2.3 -# Developed in 2019 by Travis Kessler +# v.3.3.0 +# Developed in 2020 by Travis Kessler # # Contains functions used by ecnet.Server # @@ -80,8 +80,8 @@ def default_config() -> dict: } -def get_candidate_path(prj: str, pool: int, candidate: int=None, - model: bool=False, p_best: bool=False) -> str: +def get_candidate_path(prj: str, pool: int, candidate: int = None, + model: bool = False, p_best: bool = False) -> str: '''Get path to various states of model.h5 files Args: @@ -367,8 +367,9 @@ def save_project(prj_name: str, filename: str, config_filename: str, def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, - retrain: bool=False, filename: str='model.h5', - validate: bool=True, save: bool=True, verbose: int=0) -> tuple: + retrain: bool = False, filename: str = 'model.h5', + validate: bool = True, save: bool = True, + verbose: int = 0) -> tuple: '''Trains neural network Args: @@ -385,7 +386,7 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, model only) Returns: - tuple: (error of evaluated set, list of learn/valid losses) + tuple: (error of evaluated set, tuple of learn/valid losses) ''' model = MultilayerPerceptron(filename=filename) @@ -438,7 +439,7 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str, def use_model(sets: PackagedData, dset: str, - filename: str='model.h5') -> array: + filename: str = 'model.h5') -> array: '''Uses existing model to predict data Args: diff --git a/ecnet/workflows/ecrl_workflow.py b/ecnet/workflows/ecrl_workflow.py index 5d4221e..c85e0ac 100644 --- a/ecnet/workflows/ecrl_workflow.py +++ b/ecnet/workflows/ecrl_workflow.py @@ -1,25 +1,37 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# ecnet/workflows/ecrl_workflow.py +# v.3.3.0 +# Developed in 2020 by Travis Kessler +# +# General workflow used by the UMass Lowell Energy and Combustion Research +# Laboratory +# + +# Stdlib imports +from datetime import datetime + +# 3rd party imports +from matplotlib import pyplot as plt + +# ECNet imports from ecnet import Server from ecnet.tasks.tuning import tune_hyperparameters from ecnet.tools.database import create_db from ecnet.tools.plotting import ParityPlot from ecnet.utils.data_utils import DataFrame -from ecnet.utils.error_utils import calc_med_abs_error, calc_r2 from ecnet.utils.logging import logger -from ecnet.utils.server_utils import default_config, save_config, train_model,\ - use_model +from ecnet.utils.server_utils import default_config, save_config from ecnet.workflows.workflow_utils import find_optimal_num_inputs,\ prop_range_from_split -from datetime import datetime - -from matplotlib import pyplot as plt - -def create_model(prop_abvr: str, smiles: list=None, targets: list=None, - db_name: str=None, qspr_backend: str='padel', - create_plots: bool=True, data_split: list=[0.7, 0.2, 0.1], - log_level: str='info', log_to_file: bool=True, - num_processes: int=1): +def create_model(prop_abvr: str, smiles: list = None, targets: list = None, + db_name: str = None, qspr_backend: str = 'padel', + create_plots: bool = True, data_split: list = [0.7, 0.2, 0.1], + log_level: str = 'info', log_to_file: bool = True, + num_processes: int = 1): ''' create_model: ECRL's database/model creation workflow for all publications diff --git a/ecnet/workflows/workflow_utils.py b/ecnet/workflows/workflow_utils.py index 8d0c473..6333306 100644 --- a/ecnet/workflows/workflow_utils.py +++ b/ecnet/workflows/workflow_utils.py @@ -1,10 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# ecnet/workflows/workflow_utils.py +# v.3.3.0 +# Developed in 2020 by Travis Kessler +# +# Functions used by the ECRL workflow +# + +# Stdlib imports +from multiprocessing import Pool, set_start_method +from os import name + +# ECNet imports from ecnet.utils.data_utils import DataFrame from ecnet.utils.server_utils import default_config, train_model from ecnet.tasks.limit_inputs import limit_rforest -from multiprocessing import Pool, set_start_method -from os import name - def prop_range_from_split(db_name: str, data_split: list): ''' prop_range_from_split: creates learning, validation, test subsets, each