From a2af199b5d95f25f09b7c43698f0acff3c9ddfec Mon Sep 17 00:00:00 2001 From: Joaquin Torres Bravo Date: Thu, 9 May 2024 16:04:35 +0200 Subject: [PATCH] tuning pre CS --- model_selection/hyperparam_tuning.py | 9 ++++++--- model_selection/output/hyperparam_pre_CS.xlsx | Bin 0 -> 5246 bytes 2 files changed, 6 insertions(+), 3 deletions(-) create mode 100644 model_selection/output/hyperparam_pre_CS.xlsx diff --git a/model_selection/hyperparam_tuning.py b/model_selection/hyperparam_tuning.py index fa7dc54..72e5018 100644 --- a/model_selection/hyperparam_tuning.py +++ b/model_selection/hyperparam_tuning.py @@ -148,11 +148,12 @@ if __name__ == "__main__": X = data_dic['X_train_' + method + group] y = data_dic['y_train_' + method + group] # Use group of models with class weight if needed - models = models_CS if j == 2 else models_simple + # models = models_CS if j == 2 else models_simple + models = models_CS # Save results: params and best score for each of the mdodels of this method and group hyperparam_df = pd.DataFrame(index=list(models.keys()), columns=['Parameters','Score']) for model_name, model in models.items(): - print(f"{group}-{method_names[j]}-{model_name}") + print(f"{group}-{method_names[1]}-{model_name}") # Find optimal hyperparams for curr model params = hyperparameters[model_name] search = RandomizedSearchCV(model, param_distributions=params, cv=cv, n_jobs=8, scoring='precision') @@ -165,9 +166,11 @@ if __name__ == "__main__": sheets_dict[sheet_name] = hyperparam_df # Write results to Excel file - with pd.ExcelWriter('./output/hyperparam_pre_ORIG.xlsx') as writer: + with pd.ExcelWriter('./output/hyperparam_pre_CS.xlsx') as writer: for sheet_name, data in sheets_dict.items(): data.to_excel(writer, sheet_name=sheet_name) + + print("Successful tuning") # -------------------------------------------------------------------------------------------------------- diff --git a/model_selection/output/hyperparam_pre_CS.xlsx b/model_selection/output/hyperparam_pre_CS.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..058be97509c5a493d91f5ce1da92952e652e14c9 GIT binary patch literal 5246 zcmZ`-1yodP*B(+*7zRWsK|;E_LplVhk&c-=G|13`gd-s#LrO}65<|CiBi$gOfPjFc zBOu5>>iVwt@_*l)bm3OuaXV_=5HseH9k{kJg=n#==$ z!dAQ_V?z3Ju+jq+WQg9&XLEhL8o4m&N%#;8KTWwp7!ND^A+`&*wa3;-C`U@a>|Dy{ zM&7+7keHJ}+rk7IY9d?Zd}qo5QYmV(8UEZqVO9CVdodAzuIc1COFZF7>yRvXn@AHI zYA~#KF|R_iiV;z}aB(O&Mh>ZSH(bH(HDWUGdZjwfGT4?+KCCdDkBnH(Pg zNWTjJfH2JXIPrPdL7kz$uL3{0*)=hR&x=z&*+Y%`z|hXbJ)*(2)gZXN*Ftp)Vpr$( zGaxwH$rpk|1h%S*k`E@4?#5|1rb**+Z+#zDR^8YX_dWW;YqJ>|o^a@6HOIFw-Bic7 zv|=pC0#x9?RfyVeBg)sZs4=kzbczS`gD5)RheQ%+`cMu|o2{7A@G(z+u3lI)LfAWH z-&BQI1z2#~$&VW7nYdaqR4xa6HnD4tGVbDYGNv*r_#7fv`H)eiSl`o-acdyouJoKk+SKc{|N6 zohaJ${)D6V;zUeDPwA)+Y0JU=u+FlGp5jrDZF{!67iErlTQ}?!RaUqCD5z@Ic zU^lPncjGuo793}u2{d!`O^XU<``|AU5x^fk-MG{CuM9X$VsLg1@SUFm=C2U3^V$>dzDM%LtGhc1#GgFUBg|ZAu%7KZkFw zy()~K(N-LGYvv|A_|9x<9d_463gj+H2UGMPL8FoRtb!wZr$pO2(y-e`N#*7vBWsya z?#xY#Xm9}fzN!oJ%KwS$#-*R;Lw_?{+8RKlQ`aI%{r92c_xK^dO3%1Y&%J&}0@iiq{fPwADaAxbo+Jq)*82C-AYHX&_(NS1}iQ zunlbr(X^|eXg$O1#-;s!;GL$65YZuN>9Jw7RVqvf`=)&c6iZBMX$O!?QP$>`D2;o~ zjo{ixuMt)z6g^`;I^)&A^M@rO@v>HZJ3~m%;c4V`V4Q^*=D)*{vA|Y_P;rga-JC~Y z+>1<$PWAT9kiow$>Ys#82|01cd{dpI)>tEBE0o(36uy?5`jGG~!pPy(n=oP7A_hSs zyn_Y?*>yi3BC({NX5F%o$5@wR^I{#vCn0STfxH1D`Fjn=GTGyGV$1=s_K8T-B^4Lb zuJ2_6^E1M{?6$`4Lw&slsCbT9iC1kq7k09l2fJr;EC^KULmuWEv6{hE3M}>Kkpugj zU4)wuQ&z%3g9)hEbBatwT9+;TuNsLn>!=3ydTGnDYzR|8%p1UWVMBf{Jn8WlXJM`s9r&5^jW6PJY z#2#H?N@u4M7H*X~tNNJ(Zpsyi6;IZ+rzkz_!v}^|0;WAUT(LR+T2acIZX3yFmaAiN zs|y9t8W$9Y_bul;49RL({|Z76+MXDxHxZ4@>)AkpOjW5L2kx zCQuh>n%LutE%^I*CmCdseKU?@{Fw{iT!e*t} z>F1KcbN6z=Vp67>T1Mq4H)VyPDh!_HSKpyratWShI2Bx$a9faE!m;=S;#SIg(jM^q zc@VJJrlk>e|CBXds`~LkFk{;J^g-UxrIqmJ2a^<9TLw%Af;9`cTI;# z`u&eR((;b3m=C*=tyuqFKuTBHKBQm+0Fu`LfSbP;5N}s^N4Omn>H+8bb^Eo71SBrI z&hwM^UM8~hHEXREg5;SVWWN!$&)}SGRE4Zi$)I*q+qOURkB+T^m9P+hbm zZ12x^NBhL0A}55}xU^{Lr$e#hbE>H}w~8uL=Zn@$Nj3I}@txRn-6ku`8weRm{B z^3cy;{c>_Yw^<~Se`CkH!m#PJOl2t##A9SK9!je6@#SIZnU4QPK}-hj_t2}`{a9W zUnFxRWpqj3mr6y%O2#&AMf!lW1^hIw1tpl}z$fn@x7vWZpw(N^cpp~<@2*dH8pt-Q zudrwuHc21TW05X?4aaEI2JbC6@-E0|G{Tdaij&pn!dkJ@!XdVps*-w^)6A}QBRUG) z&NJQj^&Qwck_v4zWS2>8=wd@GHTUIabM|BW`0A3m#65=RS$k^A4RO!!pu7BXv_gQw z&C9K$BpN2V*{LFqx9V^!6VcaytBPb$Qd0Bc(2er!tC{6JVVCV6{kv z(BLopJv@|!nr9Gp3o#$ZGD^O*57(DP$^Ec~Vd=%wqc84#H!HZjBIj@D7lefvIXe>r z0HnX=4EOMLg2I1JLW@QVF=_nd7gSx>60~*oiWKui$2Ah?9r72$T2ie-SS^bLiXp8n zLbaf{o$HI}iY&wd-Kz#Eh)jL%+6(#(UJ3TsVVt|^+j7vk^6h$?&TA8JQZ>gCo9jtl zuSnmnm)wfYgVZ)RaS3)Kbi^EG-aK9pg}_vH-yHL5R}M9b_&wm6$O?L^b+DGw&$%eb z;lS1LR{=sA|6<(HONAy)JUrBRtD`tHx!f9QehkKqn`!CEIrrFMYIzIo?LM^iYu_hx)2CfRqF2e1>OQW_gn1DK z3AQr6dz$(6t@M*58x$4UTUqd?0c|u0bL1O9s^z7nMJ3@6jJGwa9M0k?b6&f4Z zr|#xcKT5RsB#kjQLTH6Ve-J*wK_()&X{;j5_bPC*sUb?I%T#%Ve~3W~}Ws z19Le?ni$G&Jepw%?9?-(oUq4-DT4;GMAJXG=e74~1~}g+v2il2#5@*(w!C3*|rtgc4Q5ioe!z9$v2dC)3k-|Y*TQRaE zY|WIWH91FL-zKl`%F)RoRXE7@4OP>6L(z&0K8BJIc4U zsrFgkygV8$qLevp&EU_E7@tulWOFS)y|T%pwM=vr!))-lFds_xyL5l9L^iIjjz5bx zI(E#ZgP%k>Fn{1$37)f>QbKaJK%Tu8|AAzFsU}Zcy}Q}$$rI@b&x80SXtBu<;;PhH2ST1b{Wp%>)(TKqySn6XrTTzXHKW9` z)%KM9ce24clEy>QAh1wYiAv$8s>mv1o$>7$+<_B)gWO6b@zdpJvuvuz;4FpHcZQdu zSrn1ic`~yH3=1fhN4Fwwj;!5FbMA*|m8o=EUE>D-K-P_YO5^oMh4YFEz{}Vk0;c;c z&Z09>Py3k>>)G1~hB1PDMPlxh#v6+*y@;DzhA}@VlT*c=nT$WE>}?M@+%m`YG$f5- z$tpWMpzI7Xo)SA&e3PYeZAw%ARGgq;z-vi~+T<#_ue@xLo3EmRRfi}}DA zoZow5Qzcgy52%ZWrGc*-6mI@gOr`N7KmCFp%lyxt4npE?^4q*oTsv*Vh%WQoPp@oT zJzQTbRBB{ZkS1#8_MpGP&smG>Y;oU7!eoqml_+mNlb;HT2Zs`5GJC4&P+S&dgg>Zp zzxMG-gFiW^5B7b$uQaEAHNhrdc%UQnuDOIQ10e9bszQMZm|;WI7!7O0>c^F@WEuy` z><#XH?LGI*+!Qk-s=}F$xjq=2_yr|Ig*tJ8-m3N5=Tu>D*^J(YHO3uM8LTk+`x?tr>)}1KE>ERGl?|>WOwc^ zV(Ag-nUVPY6XraxecbNycejtcU){YhrST=pzSw&_jmnSjxlbDYK?Ws<5eybKCC>l$ ziZH7A^$Wr<{QsS!tLUrF&2KCK5QJ6!PxOC$oU8Dw_QoIh0Os}o#q78m;Ocn$cYt7G zf?omt>#%z@%GIgj?vp yt9AYde2QtU7~cLz)nAQswUqwES;F{+|D~{WHSsWR6aYYoIb<=7oRi|`)Bgc=tub8y literal 0 HcmV?d00001 -- 2.24.1