NEW UDPATES

breast-cancer-wisconsin.csv

@@ -0,0 +1,700 @@
+clump_thic,size_un,shape_un,marg_adh,epith_size,bare_nuc,bland_chr,nor_nuc,mitoses,class*
+5,1,1,1,2,1,3,1,1,Benign
+5,4,4,5,7,10,3,2,1,Benign
+3,1,1,1,2,2,3,1,1,Benign
+6,8,8,1,3,4,3,7,1,Benign
+4,1,1,3,2,1,3,1,1,Benign
+8,10,10,8,7,10,9,7,1,Malignant
+1,1,1,1,2,10,3,1,1,Benign
+2,1,2,1,2,1,3,1,1,Benign
+2,1,1,1,2,1,1,1,5,Benign
+4,2,1,1,2,1,2,1,1,Benign
+1,1,1,1,1,1,3,1,1,Benign
+2,1,1,1,2,1,2,1,1,Benign
+5,3,3,3,2,3,4,4,1,Malignant
+1,1,1,1,2,3,3,1,1,Benign
+8,7,5,10,7,9,5,5,4,Malignant
+7,4,6,4,6,1,4,3,1,Malignant
+4,1,1,1,2,1,2,1,1,Benign
+4,1,1,1,2,1,3,1,1,Benign
+10,7,7,6,4,10,4,1,2,Malignant
+6,1,1,1,2,1,3,1,1,Benign
+7,3,2,10,5,10,5,4,4,Malignant
+10,5,5,3,6,7,7,10,1,Malignant
+3,1,1,1,2,1,2,1,1,Benign
+8,4,5,1,2,4,7,3,1,Malignant
+1,1,1,1,2,1,3,1,1,Benign
+5,2,3,4,2,7,3,6,1,Malignant
+3,2,1,1,1,1,2,1,1,Benign
+5,1,1,1,2,1,2,1,1,Benign
+2,1,1,1,2,1,2,1,1,Benign
+1,1,3,1,2,1,1,1,1,Benign
+3,1,1,1,1,1,2,1,1,Benign
+2,1,1,1,2,1,3,1,1,Benign
+10,7,7,3,8,5,7,4,3,Malignant
+2,1,1,2,2,1,3,1,1,Benign
+3,1,2,1,2,1,2,1,1,Benign
+2,1,1,1,2,1,2,1,1,Benign
+10,10,10,8,6,1,8,9,1,Malignant
+6,2,1,1,1,1,7,1,1,Benign
+5,4,4,9,2,10,5,6,1,Malignant
+2,5,3,3,6,7,7,5,1,Malignant
+6,6,6,9,6,4,7,8,1,Benign
+10,4,3,1,3,3,6,5,2,Malignant
+6,10,10,2,8,10,7,3,3,Malignant
+5,6,5,6,10,1,3,1,1,Malignant
+10,10,10,4,8,1,8,10,1,Malignant
+1,1,1,1,2,1,2,1,2,Benign
+3,7,7,4,4,9,4,8,1,Malignant
+1,1,1,1,2,1,2,1,1,Benign
+4,1,1,3,2,1,3,1,1,Benign
+7,8,7,2,4,8,3,8,2,Malignant
+9,5,8,1,2,3,2,1,5,Malignant
+5,3,3,4,2,4,3,4,1,Malignant
+10,3,6,2,3,5,4,10,2,Malignant
+5,5,5,8,10,8,7,3,7,Malignant
+10,5,5,6,8,8,7,1,1,Malignant
+10,6,6,3,4,5,3,6,1,Malignant
+8,10,10,1,3,6,3,9,1,Malignant
+8,2,4,1,5,1,5,4,4,Malignant
+5,2,3,1,6,10,5,1,1,Malignant
+9,5,5,2,2,2,5,1,1,Malignant
+5,3,5,5,3,3,4,10,1,Malignant
+1,1,1,1,2,2,2,1,1,Benign
+9,10,10,1,10,8,3,3,1,Malignant
+6,3,4,1,5,2,3,9,1,Malignant
+1,1,1,1,2,1,2,1,1,Benign
+10,4,2,1,3,2,4,3,10,Malignant
+4,1,1,1,2,1,3,1,1,Benign
+5,3,4,1,8,10,4,9,1,Malignant
+8,3,8,3,4,9,8,9,8,Malignant
+1,1,1,1,2,1,3,2,1,Benign
+5,1,3,1,2,1,2,1,1,Benign
+6,10,2,8,10,2,7,8,10,Malignant
+1,3,3,2,2,1,7,2,1,Benign
+9,4,5,10,6,10,4,8,1,Malignant
+10,6,4,1,3,4,3,2,3,Malignant
+1,1,2,1,2,2,4,2,1,Benign
+1,1,4,1,2,1,2,1,1,Benign
+5,3,1,2,2,1,2,1,1,Benign
+3,1,1,1,2,3,3,1,1,Benign
+2,1,1,1,3,1,2,1,1,Benign
+2,2,2,1,1,1,7,1,1,Benign
+4,1,1,2,2,1,2,1,1,Benign
+5,2,1,1,2,1,3,1,1,Benign
+3,1,1,1,2,2,7,1,1,Benign
+3,5,7,8,8,9,7,10,7,Malignant
+5,10,6,1,10,4,4,10,10,Malignant
+3,3,6,4,5,8,4,4,1,Malignant
+3,6,6,6,5,10,6,8,3,Malignant
+4,1,1,1,2,1,3,1,1,Benign
+2,1,1,2,3,1,2,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+3,1,1,2,2,1,1,1,1,Benign
+4,1,1,1,2,1,3,1,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+2,1,1,1,2,1,3,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+2,1,1,2,2,1,1,1,1,Benign
+5,1,1,1,2,1,3,1,1,Benign
+9,6,9,2,10,6,2,9,10,Malignant
+7,5,6,10,5,10,7,9,4,Malignant
+10,3,5,1,10,5,3,10,2,Malignant
+2,3,4,4,2,5,2,5,1,Malignant
+4,1,2,1,2,1,3,1,1,Benign
+8,2,3,1,6,3,7,1,1,Malignant
+10,10,10,10,10,1,8,8,8,Malignant
+7,3,4,4,3,3,3,2,7,Malignant
+10,10,10,8,2,10,4,1,1,Malignant
+1,6,8,10,8,10,5,7,1,Malignant
+1,1,1,1,2,1,2,3,1,Benign
+6,5,4,4,3,9,7,8,3,Malignant
+1,3,1,2,2,2,5,3,2,Benign
+8,6,4,3,5,9,3,1,1,Malignant
+10,3,3,10,2,10,7,3,3,Malignant
+10,10,10,3,10,8,8,1,1,Malignant
+3,3,2,1,2,3,3,1,1,Benign
+1,1,1,1,2,5,1,1,1,Benign
+8,3,3,1,2,2,3,2,1,Benign
+4,5,5,10,4,10,7,5,8,Malignant
+1,1,1,1,4,3,1,1,1,Benign
+3,2,1,1,2,2,3,1,1,Benign
+1,1,2,2,2,1,3,1,1,Benign
+4,2,1,1,2,2,3,1,1,Benign
+10,10,10,2,10,10,5,3,3,Malignant
+5,3,5,1,8,10,5,3,1,Malignant
+5,4,6,7,9,7,8,10,1,Malignant
+1,1,1,1,2,1,2,1,1,Benign
+7,5,3,7,4,10,7,5,5,Malignant
+3,1,1,1,2,1,3,1,1,Benign
+8,3,5,4,5,10,1,6,2,Malignant
+1,1,1,1,10,1,1,1,1,Benign
+5,1,3,1,2,1,2,1,1,Benign
+2,1,1,1,2,1,3,1,1,Benign
+5,10,8,10,8,10,3,6,3,Malignant
+3,1,1,1,2,1,2,2,1,Benign
+3,1,1,1,3,1,2,1,1,Benign
+5,1,1,1,2,2,3,3,1,Benign
+4,1,1,1,2,1,2,1,1,Benign
+3,1,1,1,2,1,1,1,1,Benign
+4,1,2,1,2,1,2,1,1,Benign
+1,1,1,1,1,4,2,1,1,Benign
+3,1,1,1,2,1,1,1,1,Benign
+2,1,1,1,2,1,1,1,1,Benign
+9,5,5,4,4,5,4,3,3,Malignant
+1,1,1,1,2,5,1,1,1,Benign
+2,1,1,1,2,1,2,1,1,Benign
+1,1,3,1,2,4,2,1,1,Benign
+3,4,5,2,6,8,4,1,1,Malignant
+1,1,1,1,3,2,2,1,1,Benign
+3,1,1,3,8,1,5,8,1,Benign
+8,8,7,4,10,10,7,8,7,Malignant
+1,1,1,1,1,1,3,1,1,Benign
+7,2,4,1,6,10,5,4,3,Malignant
+10,10,8,6,4,5,8,10,1,Malignant
+4,1,1,1,2,3,1,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+5,5,5,6,3,10,3,1,1,Malignant
+1,2,2,1,2,1,2,1,1,Benign
+2,1,1,1,2,1,3,1,1,Benign
+1,1,2,1,3,4,1,1,1,Benign
+9,9,10,3,6,10,7,10,6,Malignant
+10,7,7,4,5,10,5,7,2,Malignant
+4,1,1,1,2,1,3,2,1,Benign
+3,1,1,1,2,1,3,1,1,Benign
+1,1,1,2,1,3,1,1,7,Benign
+5,1,1,1,2,4,3,1,1,Benign
+4,1,1,1,2,2,3,2,1,Benign
+5,6,7,8,8,10,3,10,3,Malignant
+10,8,10,10,6,1,3,1,10,Malignant
+3,1,1,1,2,1,3,1,1,Benign
+1,1,1,2,1,1,1,1,1,Benign
+3,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+6,10,10,10,8,10,10,10,7,Malignant
+8,6,5,4,3,10,6,1,1,Malignant
+5,8,7,7,10,10,5,7,1,Malignant
+2,1,1,1,2,1,3,1,1,Benign
+5,10,10,3,8,1,5,10,3,Malignant
+4,1,1,1,2,1,3,1,1,Benign
+5,3,3,3,6,10,3,1,1,Malignant
+1,1,1,1,1,1,3,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+6,1,1,1,2,1,3,1,1,Benign
+5,8,8,8,5,10,7,8,1,Malignant
+8,7,6,4,4,10,5,1,1,Malignant
+2,1,1,1,1,1,3,1,1,Benign
+1,5,8,6,5,8,7,10,1,Malignant
+10,5,6,10,6,10,7,7,10,Malignant
+5,8,4,10,5,8,9,10,1,Malignant
+1,2,3,1,2,1,3,1,1,Benign
+10,10,10,8,6,8,7,10,1,Malignant
+7,5,10,10,10,10,4,10,3,Malignant
+5,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+3,1,1,1,2,1,3,1,1,Benign
+4,1,1,1,2,1,3,1,1,Benign
+8,4,4,5,4,7,7,8,2,Benign
+5,1,1,4,2,1,3,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+9,7,7,5,5,10,7,8,3,Malignant
+10,8,8,4,10,10,8,1,1,Malignant
+1,1,1,1,2,1,3,1,1,Benign
+5,1,1,1,2,1,3,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+5,10,10,9,6,10,7,10,5,Malignant
+10,10,9,3,7,5,3,5,1,Malignant
+1,1,1,1,1,1,3,1,1,Benign
+1,1,1,1,1,1,3,1,1,Benign
+5,1,1,1,1,1,3,1,1,Benign
+8,10,10,10,5,10,8,10,6,Malignant
+8,10,8,8,4,8,7,7,1,Malignant
+1,1,1,1,2,1,3,1,1,Benign
+10,10,10,10,7,10,7,10,4,Malignant
+10,10,10,10,3,10,10,6,1,Malignant
+8,7,8,7,5,5,5,10,2,Malignant
+1,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+6,10,7,7,6,4,8,10,2,Malignant
+6,1,3,1,2,1,3,1,1,Benign
+1,1,1,2,2,1,3,1,1,Benign
+10,6,4,3,10,10,9,10,1,Malignant
+4,1,1,3,1,5,2,1,1,Malignant
+7,5,6,3,3,8,7,4,1,Malignant
+10,5,5,6,3,10,7,9,2,Malignant
+1,1,1,1,2,1,2,1,1,Benign
+10,5,7,4,4,10,8,9,1,Malignant
+8,9,9,5,3,5,7,7,1,Malignant
+1,1,1,1,1,1,3,1,1,Benign
+10,10,10,3,10,10,9,10,1,Malignant
+7,4,7,4,3,7,7,6,1,Malignant
+6,8,7,5,6,8,8,9,2,Malignant
+8,4,6,3,3,1,4,3,1,Benign
+10,4,5,5,5,10,4,1,1,Malignant
+3,3,2,1,3,1,3,6,1,Benign
+3,1,4,1,2,4,3,1,1,Benign
+10,8,8,2,8,10,4,8,10,Malignant
+9,8,8,5,6,2,4,10,4,Malignant
+8,10,10,8,6,9,3,10,10,Malignant
+10,4,3,2,3,10,5,3,2,Malignant
+5,1,3,3,2,2,2,3,1,Benign
+3,1,1,3,1,1,3,1,1,Benign
+2,1,1,1,2,1,3,1,1,Benign
+1,1,1,1,2,5,5,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+5,1,1,2,2,2,3,1,1,Benign
+8,10,10,8,5,10,7,8,1,Malignant
+8,4,4,1,2,9,3,3,1,Malignant
+4,1,1,1,2,1,3,6,1,Benign
+3,1,1,1,2,4,3,1,1,Benign
+1,2,2,1,2,1,1,1,1,Benign
+10,4,4,10,2,10,5,3,3,Malignant
+6,3,3,5,3,10,3,5,3,Benign
+6,10,10,2,8,10,7,3,3,Malignant
+9,10,10,1,10,8,3,3,1,Malignant
+5,6,6,2,4,10,3,6,1,Malignant
+3,1,1,1,2,1,1,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+3,1,1,1,2,1,3,1,1,Benign
+5,7,7,1,5,8,3,4,1,Benign
+10,5,8,10,3,10,5,1,3,Malignant
+5,10,10,6,10,10,10,6,5,Malignant
+8,8,9,4,5,10,7,8,1,Malignant
+10,4,4,10,6,10,5,5,1,Malignant
+7,9,4,10,10,3,5,3,3,Malignant
+5,1,4,1,2,1,3,2,1,Benign
+10,10,6,3,3,10,4,3,2,Malignant
+3,3,5,2,3,10,7,1,1,Malignant
+10,8,8,2,3,4,8,7,8,Malignant
+1,1,1,1,2,1,3,1,1,Benign
+8,4,7,1,3,10,3,9,2,Malignant
+5,1,1,1,2,1,3,1,1,Benign
+3,3,5,2,3,10,7,1,1,Malignant
+7,2,4,1,3,4,3,3,1,Malignant
+3,1,1,1,2,1,3,2,1,Benign
+3,1,3,1,2,4,2,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+10,5,7,3,3,7,3,3,8,Malignant
+3,1,1,1,2,1,3,1,1,Benign
+2,1,1,2,2,1,3,1,1,Benign
+1,4,3,10,4,10,5,6,1,Malignant
+10,4,6,1,2,10,5,3,1,Malignant
+7,4,5,10,2,10,3,8,2,Malignant
+8,10,10,10,8,10,10,7,3,Malignant
+10,10,10,10,10,10,4,10,10,Malignant
+3,1,1,1,3,1,2,1,1,Benign
+6,1,3,1,4,5,5,10,1,Malignant
+5,6,6,8,6,10,4,10,4,Malignant
+1,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+8,8,8,1,2,4,6,10,1,Malignant
+10,4,4,6,2,10,2,3,1,Malignant
+1,1,1,1,2,4,2,1,1,Benign
+5,5,7,8,6,10,7,4,1,Malignant
+5,3,4,3,4,5,4,7,1,Benign
+5,4,3,1,2,4,2,3,1,Benign
+8,2,1,1,5,1,1,1,1,Benign
+9,1,2,6,4,10,7,7,2,Malignant
+8,4,10,5,4,4,7,10,1,Malignant
+1,1,1,1,2,1,3,1,1,Benign
+10,10,10,7,9,10,7,10,10,Malignant
+1,1,1,1,2,1,3,1,1,Benign
+8,3,4,9,3,10,3,3,1,Malignant
+10,8,4,4,4,10,3,10,4,Malignant
+1,1,1,1,2,1,3,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+7,8,7,6,4,3,8,8,4,Malignant
+3,1,1,1,2,5,5,1,1,Benign
+2,1,1,1,3,1,2,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+8,6,4,10,10,1,3,5,1,Malignant
+1,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,1,1,2,1,1,Benign
+4,6,5,6,7,4,4,9,1,Benign
+5,5,5,2,5,10,4,3,1,Malignant
+6,8,7,8,6,8,8,9,1,Malignant
+1,1,1,1,5,1,3,1,1,Benign
+4,4,4,4,6,5,7,3,1,Benign
+7,6,3,2,5,10,7,4,6,Malignant
+3,1,1,1,2,4,3,1,1,Benign
+3,1,1,1,2,1,3,1,1,Benign
+5,4,6,10,2,10,4,1,1,Malignant
+1,1,1,1,2,1,3,1,1,Benign
+3,2,2,1,2,1,2,3,1,Benign
+10,1,1,1,2,10,5,4,1,Malignant
+1,1,1,1,2,1,2,1,1,Benign
+8,10,3,2,6,4,3,10,1,Malignant
+10,4,6,4,5,10,7,1,1,Malignant
+10,4,7,2,2,8,6,1,1,Malignant
+5,1,1,1,2,1,3,1,2,Benign
+5,2,2,2,2,1,2,2,1,Benign
+5,4,6,6,4,10,4,3,1,Malignant
+8,6,7,3,3,10,3,4,2,Malignant
+1,1,1,1,2,1,1,1,1,Benign
+6,5,5,8,4,10,3,4,1,Malignant
+1,1,1,1,2,1,3,1,1,Benign
+1,1,1,1,1,1,2,1,1,Benign
+8,5,5,5,2,10,4,3,1,Malignant
+10,3,3,1,2,10,7,6,1,Malignant
+1,1,1,1,2,1,3,1,1,Benign
+2,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+7,6,4,8,10,10,9,5,3,Malignant
+1,1,1,1,2,1,1,1,1,Benign
+5,2,2,2,3,1,1,3,1,Benign
+1,1,1,1,1,1,1,3,1,Benign
+3,4,4,10,5,1,3,3,1,Malignant
+4,2,3,5,3,8,7,6,1,Malignant
+5,1,1,3,2,1,1,1,1,Benign
+2,1,1,1,2,1,3,1,1,Benign
+3,4,5,3,7,3,4,6,1,Benign
+2,7,10,10,7,10,4,9,4,Malignant
+1,1,1,1,2,1,2,1,1,Benign
+4,1,1,1,3,1,2,2,1,Benign
+5,3,3,1,3,3,3,3,3,Malignant
+8,10,10,7,10,10,7,3,8,Malignant
+8,10,5,3,8,4,4,10,3,Malignant
+10,3,5,4,3,7,3,5,3,Malignant
+6,10,10,10,10,10,8,10,10,Malignant
+3,10,3,10,6,10,5,1,4,Malignant
+3,2,2,1,4,3,2,1,1,Benign
+4,4,4,2,2,3,2,1,1,Benign
+2,1,1,1,2,1,3,1,1,Benign
+2,1,1,1,2,1,2,1,1,Benign
+6,10,10,10,8,10,7,10,7,Malignant
+5,8,8,10,5,10,8,10,3,Malignant
+1,1,3,1,2,1,1,1,1,Benign
+1,1,3,1,1,1,2,1,1,Benign
+4,3,2,1,3,1,2,1,1,Benign
+1,1,3,1,2,1,1,1,1,Benign
+4,1,2,1,2,1,2,1,1,Benign
+5,1,1,2,2,1,2,1,1,Benign
+3,1,2,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,1,1,2,1,1,Benign
+3,1,1,4,3,1,2,2,1,Benign
+5,3,4,1,4,1,3,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+10,6,3,6,4,10,7,8,4,Malignant
+3,2,2,2,2,1,3,2,1,Benign
+2,1,1,1,2,1,1,1,1,Benign
+2,1,1,1,2,1,1,1,1,Benign
+3,3,2,2,3,1,1,2,3,Benign
+7,6,6,3,2,10,7,1,1,Malignant
+5,3,3,2,3,1,3,1,1,Benign
+2,1,1,1,2,1,2,2,1,Benign
+5,1,1,1,3,2,2,2,1,Benign
+1,1,1,2,2,1,2,1,1,Benign
+10,8,7,4,3,10,7,9,1,Malignant
+3,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,1,1,1,1,1,Benign
+1,2,3,1,2,1,2,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+3,1,1,1,2,1,3,1,1,Benign
+4,1,1,1,2,1,1,1,1,Benign
+3,2,1,1,2,1,2,2,1,Benign
+1,2,3,1,2,1,1,1,1,Benign
+3,10,8,7,6,9,9,3,8,Malignant
+3,1,1,1,2,1,1,1,1,Benign
+5,3,3,1,2,1,2,1,1,Benign
+3,1,1,1,2,4,1,1,1,Benign
+1,2,1,3,2,1,1,2,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+4,2,2,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+2,3,2,2,2,2,3,1,1,Benign
+3,1,2,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,1,4,2,1,1,Benign
+10,10,10,6,8,4,8,5,1,Malignant
+5,1,2,1,2,1,3,1,1,Benign
+8,5,6,2,3,10,6,6,1,Malignant
+3,3,2,6,3,3,3,5,1,Benign
+8,7,8,5,10,10,7,2,1,Malignant
+1,1,1,1,2,1,2,1,1,Benign
+5,2,2,2,2,2,3,2,2,Benign
+2,3,1,1,5,1,1,1,1,Benign
+3,2,2,3,2,3,3,1,1,Benign
+10,10,10,7,10,10,8,2,1,Malignant
+4,3,3,1,2,1,3,3,1,Benign
+5,1,3,1,2,1,2,1,1,Benign
+3,1,1,1,2,1,1,1,1,Benign
+9,10,10,10,10,10,10,10,1,Malignant
+5,3,6,1,2,1,1,1,1,Benign
+8,7,8,2,4,2,5,10,1,Malignant
+1,1,1,1,2,1,2,1,1,Benign
+2,1,1,1,2,1,2,1,1,Benign
+1,3,1,1,2,1,2,2,1,Benign
+5,1,1,3,4,1,3,2,1,Benign
+5,1,1,1,2,1,2,2,1,Benign
+3,2,2,3,2,1,1,1,1,Benign
+6,9,7,5,5,8,4,2,1,Benign
+10,8,10,1,3,10,5,1,1,Malignant
+10,10,10,1,6,1,2,8,1,Malignant
+4,1,1,1,2,1,1,1,1,Benign
+4,1,3,3,2,1,1,1,1,Benign
+5,1,1,1,2,1,1,1,1,Benign
+10,4,3,10,4,10,10,1,1,Malignant
+5,2,2,4,2,4,1,1,1,Benign
+1,1,1,3,2,3,1,1,1,Benign
+1,1,1,1,2,2,1,1,1,Benign
+5,1,1,6,3,1,2,1,1,Benign
+2,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+5,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,1,1,1,1,1,Benign
+5,7,9,8,6,10,8,10,1,Malignant
+4,1,1,3,1,1,2,1,1,Benign
+5,1,1,1,2,1,1,1,1,Benign
+3,1,1,3,2,1,1,1,1,Benign
+4,5,5,8,6,10,10,7,1,Malignant
+2,3,1,1,3,1,1,1,1,Benign
+10,2,2,1,2,6,1,1,2,Malignant
+10,6,5,8,5,10,8,6,1,Malignant
+8,8,9,6,6,3,10,10,1,Malignant
+5,1,2,1,2,1,1,1,1,Benign
+5,1,3,1,2,1,1,1,1,Benign
+5,1,1,3,2,1,1,1,1,Benign
+3,1,1,1,2,5,1,1,1,Benign
+6,1,1,3,2,1,1,1,1,Benign
+4,1,1,1,2,1,1,2,1,Benign
+4,1,1,1,2,1,1,1,1,Benign
+10,9,8,7,6,4,7,10,3,Malignant
+10,6,6,2,4,10,9,7,1,Malignant
+6,6,6,5,4,10,7,6,2,Malignant
+4,1,1,1,2,1,1,1,1,Benign
+1,1,2,1,2,1,2,1,1,Benign
+3,1,1,1,1,1,2,1,1,Benign
+6,1,1,3,2,1,1,1,1,Benign
+6,1,1,1,1,1,1,1,1,Benign
+4,1,1,1,2,1,1,1,1,Benign
+5,1,1,1,2,1,1,1,1,Benign
+3,1,1,1,2,1,1,1,1,Benign
+4,1,2,1,2,1,1,1,1,Benign
+4,1,1,1,2,1,1,1,1,Benign
+5,2,1,1,2,1,1,1,1,Benign
+4,8,7,10,4,10,7,5,1,Malignant
+5,1,1,1,1,1,1,1,1,Benign
+5,3,2,4,2,1,1,1,1,Benign
+9,10,10,10,10,5,10,10,10,Malignant
+8,7,8,5,5,10,9,10,1,Malignant
+5,1,2,1,2,1,1,1,1,Benign
+1,1,1,3,1,3,1,1,1,Benign
+3,1,1,1,1,1,2,1,1,Benign
+10,10,10,10,6,10,8,1,5,Malignant
+3,6,4,10,3,3,3,4,1,Malignant
+6,3,2,1,3,4,4,1,1,Malignant
+1,1,1,1,2,1,1,1,1,Benign
+5,8,9,4,3,10,7,1,1,Malignant
+4,1,1,1,1,1,2,1,1,Benign
+5,10,10,10,6,10,6,5,2,Malignant
+5,1,2,10,4,5,2,1,1,Benign
+3,1,1,1,1,1,2,1,1,Benign
+1,1,1,1,1,1,1,1,1,Benign
+4,2,1,1,2,1,1,1,1,Benign
+4,1,1,1,2,1,2,1,1,Benign
+4,1,1,1,2,1,2,1,1,Benign
+6,1,1,1,2,1,3,1,1,Benign
+4,1,1,1,2,1,2,1,1,Benign
+4,1,1,2,2,1,2,1,1,Benign
+4,1,1,1,2,1,3,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+3,3,1,1,2,1,1,1,1,Benign
+8,10,10,10,7,5,4,8,7,Malignant
+1,1,1,1,2,4,1,1,1,Benign
+5,1,1,1,2,1,1,1,1,Benign
+2,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+5,1,1,1,2,1,2,1,1,Benign
+5,1,1,1,2,1,1,1,1,Benign
+3,1,1,1,1,1,2,1,1,Benign
+6,6,7,10,3,10,8,10,2,Malignant
+4,10,4,7,3,10,9,10,1,Malignant
+1,1,1,1,1,1,1,1,1,Benign
+1,1,1,1,1,1,2,1,1,Benign
+3,1,2,2,2,1,1,1,1,Benign
+4,7,8,3,4,10,9,1,1,Malignant
+1,1,1,1,3,1,1,1,1,Benign
+4,1,1,1,3,1,1,1,1,Benign
+10,4,5,4,3,5,7,3,1,Malignant
+7,5,6,10,4,10,5,3,1,Malignant
+3,1,1,1,2,1,2,1,1,Benign
+3,1,1,2,2,1,1,1,1,Benign
+4,1,1,1,2,1,1,1,1,Benign
+4,1,1,1,2,1,3,1,1,Benign
+6,1,3,2,2,1,1,1,1,Benign
+4,1,1,1,1,1,2,1,1,Benign
+7,4,4,3,4,10,6,9,1,Malignant
+4,2,2,1,2,1,2,1,1,Benign
+1,1,1,1,1,1,3,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+2,1,1,1,2,1,2,1,1,Benign
+1,1,3,2,2,1,3,1,1,Benign
+5,1,1,1,2,1,3,1,1,Benign
+5,1,2,1,2,1,3,1,1,Benign
+4,1,1,1,2,1,2,1,1,Benign
+6,1,1,1,2,1,2,1,1,Benign
+5,1,1,1,2,2,2,1,1,Benign
+3,1,1,1,2,1,1,1,1,Benign
+5,3,1,1,2,1,1,1,1,Benign
+4,1,1,1,2,1,2,1,1,Benign
+2,1,3,2,2,1,2,1,1,Benign
+5,1,1,1,2,1,2,1,1,Benign
+6,10,10,10,4,10,7,10,1,Malignant
+2,1,1,1,1,1,1,1,1,Benign
+3,1,1,1,1,1,1,1,1,Benign
+7,8,3,7,4,5,7,8,2,Malignant
+3,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+3,2,2,2,2,1,4,2,1,Benign
+4,4,2,1,2,5,2,1,2,Benign
+3,1,1,1,2,1,1,1,1,Benign
+4,3,1,1,2,1,4,8,1,Benign
+5,2,2,2,1,1,2,1,1,Benign
+5,1,1,3,2,1,1,1,1,Benign
+2,1,1,1,2,1,2,1,1,Benign
+5,1,1,1,2,1,2,1,1,Benign
+5,1,1,1,2,1,3,1,1,Benign
+5,1,1,1,2,1,3,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+4,1,1,1,2,1,3,2,1,Benign
+5,7,10,10,5,10,10,10,1,Malignant
+3,1,2,1,2,1,3,1,1,Benign
+4,1,1,1,2,3,2,1,1,Benign
+8,4,4,1,6,10,2,5,2,Malignant
+10,10,8,10,6,5,10,3,1,Malignant
+8,10,4,4,8,10,8,2,1,Malignant
+7,6,10,5,3,10,9,10,2,Malignant
+3,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+10,9,7,3,4,2,7,7,1,Malignant
+5,1,2,1,2,1,3,1,1,Benign
+5,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,3,1,1,Benign
+5,1,2,1,2,1,2,1,1,Benign
+5,7,10,6,5,10,7,5,1,Malignant
+6,10,5,5,4,10,6,10,1,Malignant
+3,1,1,1,2,1,1,1,1,Benign
+5,1,1,6,3,1,1,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+8,10,10,10,6,10,10,10,1,Malignant
+5,1,1,1,2,1,2,2,1,Benign
+9,8,8,9,6,3,4,1,1,Malignant
+5,1,1,1,2,1,1,1,1,Benign
+4,10,8,5,4,1,10,1,1,Malignant
+2,5,7,6,4,10,7,6,1,Malignant
+10,3,4,5,3,10,4,1,1,Malignant
+5,1,2,1,2,1,1,1,1,Benign
+4,8,6,3,4,10,7,1,1,Malignant
+5,1,1,1,2,1,2,1,1,Benign
+4,1,2,1,2,1,2,1,1,Benign
+5,1,3,1,2,1,3,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+5,2,4,1,1,1,1,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,1,1,2,1,1,Benign
+4,1,1,1,2,1,2,1,1,Benign
+5,4,6,8,4,1,8,10,1,Malignant
+5,3,2,8,5,10,8,1,2,Malignant
+10,5,10,3,5,8,7,8,3,Malignant
+4,1,1,2,2,1,1,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+5,10,10,10,10,10,10,1,1,Malignant
+5,1,1,1,2,1,1,1,1,Benign
+10,4,3,10,3,10,7,1,2,Malignant
+5,10,10,10,5,2,8,5,1,Malignant
+8,10,10,10,6,10,10,10,10,Malignant
+2,3,1,1,2,1,2,1,1,Benign
+2,1,1,1,1,1,2,1,1,Benign
+4,1,3,1,2,1,2,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,1,4,1,1,1,Benign
+4,1,1,1,2,1,2,1,1,Benign
+5,1,1,1,2,1,2,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+6,3,3,3,3,2,6,1,1,Benign
+7,1,2,3,2,1,2,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+5,1,1,2,1,1,2,1,1,Benign
+3,1,3,1,3,4,1,1,1,Benign
+4,6,6,5,7,6,7,7,3,Malignant
+2,1,1,1,2,5,1,1,1,Benign
+2,1,1,1,2,1,1,1,1,Benign
+4,1,1,1,2,1,1,1,1,Benign
+6,2,3,1,2,1,1,1,1,Benign
+5,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+8,7,4,4,5,3,5,10,1,Malignant
+3,1,1,1,2,1,1,1,1,Benign
+3,1,4,1,2,1,1,1,1,Benign
+10,10,7,8,7,1,10,10,3,Malignant
+4,2,4,3,2,2,2,1,1,Benign
+4,1,1,1,2,1,1,1,1,Benign
+5,1,1,3,2,1,1,1,1,Benign
+4,1,1,3,2,1,1,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+2,1,1,1,2,1,1,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+1,2,2,1,2,1,1,1,1,Benign
+1,1,1,3,2,1,1,1,1,Benign
+5,10,10,10,10,2,10,10,10,Malignant
+3,1,1,1,2,1,2,1,1,Benign
+3,1,1,2,3,4,1,1,1,Benign
+1,2,1,3,2,1,2,1,1,Benign
+5,1,1,1,2,1,2,2,1,Benign
+4,1,1,1,2,1,2,1,1,Benign
+3,1,1,1,2,1,3,1,1,Benign
+3,1,1,1,2,1,2,1,1,Benign
+5,1,1,1,2,1,2,1,1,Benign
+5,4,5,1,8,1,3,6,1,Benign
+7,8,8,7,3,10,7,2,3,Malignant
+1,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+4,1,1,1,2,1,3,1,1,Benign
+1,1,3,1,2,1,2,1,1,Benign
+1,1,3,1,2,1,2,1,1,Benign
+3,1,1,3,2,1,2,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+5,2,2,2,2,1,1,1,2,Benign
+3,1,1,1,2,1,3,1,1,Benign
+5,7,4,1,6,1,7,10,3,Malignant
+5,10,10,8,5,5,7,10,1,Malignant
+3,10,7,8,5,8,7,4,1,Malignant
+3,2,1,2,2,1,3,1,1,Benign
+2,1,1,1,2,1,3,1,1,Benign
+5,3,2,1,3,1,1,1,1,Benign
+1,1,1,1,2,1,2,1,1,Benign
+4,1,4,1,2,1,1,1,1,Benign
+1,1,2,1,2,1,2,1,1,Benign
+5,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+2,1,1,1,2,1,1,1,1,Benign
+10,10,10,10,5,10,10,10,7,Malignant
+5,10,10,10,4,10,5,6,3,Malignant
+5,1,1,1,2,1,3,2,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,1,1,1,Benign
+3,1,1,1,2,1,2,3,1,Benign
+4,1,1,1,2,1,1,1,1,Benign
+1,1,1,1,2,1,1,1,8,Benign
+1,1,1,3,2,1,1,1,1,Benign
+5,10,10,5,4,5,4,4,1,Malignant
+3,1,1,1,2,1,1,1,1,Benign
+3,1,1,1,2,1,2,1,2,Benign
+3,1,1,1,3,2,1,1,1,Benign
+2,1,1,1,2,1,1,1,1,Benign
+5,10,10,3,7,3,8,10,2,Malignant
+4,8,6,4,3,4,10,6,1,Malignant
+4,8,8,5,4,5,10,4,1,Malignant

cachedir/joblib/run/GridSearchForModels/03316dae023356b400b64061e59b4761/metadata.json

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cachedir/joblib/run/GridSearchForModels/3294c2d73e0b69b7e2c3488c95dbb85d/metadata.json

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cachedir/joblib/run/GridSearchForModels/37519dd894d05d61acea33c7629ad5df/metadata.json

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cachedir/joblib/run/GridSearchForModels/46b13a7726ed8eeee91902d7222493c3/metadata.json

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cachedir/joblib/run/GridSearchForModels/5d1c5fed33f062d128fe27abdff1a588/metadata.json

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cachedir/joblib/run/GridSearchForModels/74ee6e4e2d4e95a538931d6c565590f3/metadata.json

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cachedir/joblib/run/GridSearchForModels/865dc4b6eb9ed2491a5fe265ff3a53c2/metadata.json

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cachedir/joblib/run/GridSearchForModels/9e693891c6d22c678734887be2a6282f/metadata.json

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cachedir/joblib/run/GridSearchForModels/a46dcd4e232a8dcae4536a2e2da671aa/metadata.json

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cachedir/joblib/run/GridSearchForModels/ab217966ac94ecfcb1f16130f136aadd/metadata.json

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cachedir/joblib/run/GridSearchForModels/b26d47976b47db54818d974819f582b9/metadata.json

@@ -0,0 +1 @@
+{"duration": 286.56338810920715, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "LogisticRegression(C=1.925, class_weight=None, dual=False, fit_intercept=True,\n                   intercept_scaling=1, l1_ratio=None, max_iter=200,\n                   multi_class='auto', n_jobs=None, penalty='none',\n                   random_state=None, solver='saga', tol=0.0001, verbose=0,\n                   warm_start=False)", "params": "{'C': [0.5, 0.575, 0.6499999999999999, 0.7249999999999999, 0.7999999999999998, 0.8749999999999998, 0.9499999999999997, 1.0249999999999997, 1.0999999999999996, 1.1749999999999996, 1.2499999999999996, 1.3249999999999995, 1.3999999999999995, 1.4749999999999994, 1.5499999999999994, 1.6249999999999993, 1.6999999999999993, 1.7749999999999992, 1.8499999999999992, 1.9249999999999992], 'max_iter': [50, 100, 150, 200], 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}", "eachAlgor": "'LR'", "AlgorithmsIDsEnd": "1356"}}

cachedir/joblib/run/GridSearchForModels/dc2413a05bbf24b34447fc94cb3166f4/metadata.json

@@ -0,0 +1 @@
+{"duration": 515.3227281570435, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "SVC(C=4.39, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n    decision_function_shape='ovr', degree=3, gamma='scale', kernel='sigmoid',\n    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n    verbose=False)", "params": "{'C': [0.1, 0.21000000000000002, 0.32000000000000006, 0.43000000000000005, 0.54, 0.65, 0.7600000000000001, 0.8700000000000001, 0.9800000000000001, 1.09, 1.2000000000000002, 1.3100000000000003, 1.4200000000000004, 1.5300000000000002, 1.6400000000000003, 1.7500000000000002, 1.8600000000000003, 1.9700000000000004, 2.08, 2.1900000000000004, 2.3000000000000003, 2.4100000000000006, 2.5200000000000005, 2.6300000000000003, 2.7400000000000007, 2.8500000000000005, 2.9600000000000004, 3.0700000000000003, 3.1800000000000006, 3.2900000000000005, 3.4000000000000004, 3.5100000000000007, 3.6200000000000006, 3.7300000000000004, 3.8400000000000007, 3.9500000000000006, 4.0600000000000005, 4.17, 4.28, 4.390000000000001], 'kernel': ['rbf', 'linear', 'poly', 'sigmoid']}", "eachAlgor": "'SVC'", "AlgorithmsIDsEnd": "576"}}

cachedir/joblib/run/GridSearchForModels/ec1b794b874ed8bd2511d49545431aa6/metadata.json

@@ -0,0 +1 @@
+{"duration": 610.0474598407745, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n                     criterion='entropy', max_depth=None, max_features='auto',\n                     max_leaf_nodes=None, max_samples=None,\n                     min_impurity_decrease=0.0, min_impurity_split=None,\n                     min_samples_leaf=1, min_samples_split=2,\n                     min_weight_fraction_leaf=0.0, n_estimators=139,\n                     n_jobs=None, oob_score=False, random_state=None, verbose=0,\n                     warm_start=False)", "params": "{'n_estimators': [60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'ExtraT'", "AlgorithmsIDsEnd": "2606"}}

cachedir/joblib/run/GridSearchForModels/f37dfc7f4090db9277c351e400a7579c/metadata.json

@@ -1 +1 @@
-{"duration": 396.95492720603943, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "AdaBoostClassifier(algorithm='SAMME', base_estimator=None, learning_rate=1.2,\n                   n_estimators=79, random_state=None)", "params": "{'n_estimators': [40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79], 'learning_rate': [0.1, 1.2000000000000002], 'algorithm': ['SAMME.R', 'SAMME']}", "eachAlgor": "'AdaB'", "AlgorithmsIDsEnd": "2766"}}
+{"duration": 243.32867527008057, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "MLPClassifier(activation='tanh', alpha=0.00081, batch_size='auto', beta_1=0.9,\n              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n              hidden_layer_sizes=(100,), learning_rate='constant',\n              learning_rate_init=0.001, max_fun=15000, max_iter=100,\n              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n              power_t=0.5, random_state=None, shuffle=True, solver='sgd',\n              tol=0.00081, validation_fraction=0.1, verbose=False,\n              warm_start=False)", "params": "{'alpha': [1e-05, 0.00021, 0.00041000000000000005, 0.0006100000000000001, 0.0008100000000000001], 'tol': [1e-05, 0.00041000000000000005, 0.0008100000000000001], 'max_iter': [100], 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver': ['adam', 'sgd']}", "eachAlgor": "'MLP'", "AlgorithmsIDsEnd": "1236"}}

cachedir/joblib/run/GridSearchForModels/func_code.py

@@ -1,7 +1,7 @@
-# first line: 542
+# first line: 556
 @memory.cache
 def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
-    print('test')
+    print('start')
     # instantiate spark session
     spark = (   
         SparkSession    
@@ -82,6 +82,12 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
 
     loop = 10
 
+    # influence calculation for all the instances
+    inputs = range(len(XData))
+    num_cores = multiprocessing.cpu_count()
+    
+    impDataInst = Parallel(n_jobs=num_cores)(delayed(processInput)(i,XData,yData,crossValidation,clf) for i in inputs)
+
     for eachModelParameters in parametersLocalNew:
         clf.set_params(**eachModelParameters)
 
@@ -177,8 +183,10 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
     results.append(PerFeatureAccuracyPandas) # Position: 3 and so on
     results.append(perm_imp_eli5PD) # Position: 4 and so on
     results.append(featureScores) # Position: 5 and so on
+    metrics = metrics.clip(lower=0)
     metrics = metrics.to_json()
     results.append(metrics) # Position: 6 and so on
     results.append(perModelProbPandas) # Position: 7 and so on
+    results.append(json.dumps(impDataInst)) # Position: 8 and so on
 
     return results

diabetes.csv

@@ -0,0 +1,769 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DPF,Age,Outcome*
+6,148,72,35,0,33.6,0.627,50,Positive
+1,85,66,29,0,26.6,0.351,31,Negative
+8,183,64,0,0,23.3,0.672,32,Positive
+1,89,66,23,94,28.1,0.167,21,Negative
+0,137,40,35,168,43.1,2.288,33,Positive
+5,116,74,0,0,25.6,0.201,30,Negative
+3,78,50,32,88,31,0.248,26,Positive
+10,115,0,0,0,35.3,0.134,29,Negative
+2,197,70,45,543,30.5,0.158,53,Positive
+8,125,96,0,0,0,0.232,54,Positive
+4,110,92,0,0,37.6,0.191,30,Negative
+10,168,74,0,0,38,0.537,34,Positive
+10,139,80,0,0,27.1,1.441,57,Negative
+1,189,60,23,846,30.1,0.398,59,Positive
+5,166,72,19,175,25.8,0.587,51,Positive
+7,100,0,0,0,30,0.484,32,Positive
+0,118,84,47,230,45.8,0.551,31,Positive
+7,107,74,0,0,29.6,0.254,31,Positive
+1,103,30,38,83,43.3,0.183,33,Negative
+1,115,70,30,96,34.6,0.529,32,Positive
+3,126,88,41,235,39.3,0.704,27,Negative
+8,99,84,0,0,35.4,0.388,50,Negative
+7,196,90,0,0,39.8,0.451,41,Positive
+9,119,80,35,0,29,0.263,29,Positive
+11,143,94,33,146,36.6,0.254,51,Positive
+10,125,70,26,115,31.1,0.205,41,Positive
+7,147,76,0,0,39.4,0.257,43,Positive
+1,97,66,15,140,23.2,0.487,22,Negative
+13,145,82,19,110,22.2,0.245,57,Negative
+5,117,92,0,0,34.1,0.337,38,Negative
+5,109,75,26,0,36,0.546,60,Negative
+3,158,76,36,245,31.6,0.851,28,Positive
+3,88,58,11,54,24.8,0.267,22,Negative
+6,92,92,0,0,19.9,0.188,28,Negative
+10,122,78,31,0,27.6,0.512,45,Negative
+4,103,60,33,192,24,0.966,33,Negative
+11,138,76,0,0,33.2,0.42,35,Negative
+9,102,76,37,0,32.9,0.665,46,Positive
+2,90,68,42,0,38.2,0.503,27,Positive
+4,111,72,47,207,37.1,1.39,56,Positive
+3,180,64,25,70,34,0.271,26,Negative
+7,133,84,0,0,40.2,0.696,37,Negative
+7,106,92,18,0,22.7,0.235,48,Negative
+9,171,110,24,240,45.4,0.721,54,Positive
+7,159,64,0,0,27.4,0.294,40,Negative
+0,180,66,39,0,42,1.893,25,Positive
+1,146,56,0,0,29.7,0.564,29,Negative
+2,71,70,27,0,28,0.586,22,Negative
+7,103,66,32,0,39.1,0.344,31,Positive
+7,105,0,0,0,0,0.305,24,Negative
+1,103,80,11,82,19.4,0.491,22,Negative
+1,101,50,15,36,24.2,0.526,26,Negative
+5,88,66,21,23,24.4,0.342,30,Negative
+8,176,90,34,300,33.7,0.467,58,Positive
+7,150,66,42,342,34.7,0.718,42,Negative
+1,73,50,10,0,23,0.248,21,Negative
+7,187,68,39,304,37.7,0.254,41,Positive
+0,100,88,60,110,46.8,0.962,31,Negative
+0,146,82,0,0,40.5,1.781,44,Negative
+0,105,64,41,142,41.5,0.173,22,Negative
+2,84,0,0,0,0,0.304,21,Negative
+8,133,72,0,0,32.9,0.27,39,Positive
+5,44,62,0,0,25,0.587,36,Negative
+2,141,58,34,128,25.4,0.699,24,Negative
+7,114,66,0,0,32.8,0.258,42,Positive
+5,99,74,27,0,29,0.203,32,Negative
+0,109,88,30,0,32.5,0.855,38,Positive
+2,109,92,0,0,42.7,0.845,54,Negative
+1,95,66,13,38,19.6,0.334,25,Negative
+4,146,85,27,100,28.9,0.189,27,Negative
+2,100,66,20,90,32.9,0.867,28,Positive
+5,139,64,35,140,28.6,0.411,26,Negative
+13,126,90,0,0,43.4,0.583,42,Positive
+4,129,86,20,270,35.1,0.231,23,Negative
+1,79,75,30,0,32,0.396,22,Negative
+1,0,48,20,0,24.7,0.14,22,Negative
+7,62,78,0,0,32.6,0.391,41,Negative
+5,95,72,33,0,37.7,0.37,27,Negative
+0,131,0,0,0,43.2,0.27,26,Positive
+2,112,66,22,0,25,0.307,24,Negative
+3,113,44,13,0,22.4,0.14,22,Negative
+2,74,0,0,0,0,0.102,22,Negative
+7,83,78,26,71,29.3,0.767,36,Negative
+0,101,65,28,0,24.6,0.237,22,Negative
+5,137,108,0,0,48.8,0.227,37,Positive
+2,110,74,29,125,32.4,0.698,27,Negative
+13,106,72,54,0,36.6,0.178,45,Negative
+2,100,68,25,71,38.5,0.324,26,Negative
+15,136,70,32,110,37.1,0.153,43,Positive
+1,107,68,19,0,26.5,0.165,24,Negative
+1,80,55,0,0,19.1,0.258,21,Negative
+4,123,80,15,176,32,0.443,34,Negative
+7,81,78,40,48,46.7,0.261,42,Negative
+4,134,72,0,0,23.8,0.277,60,Positive
+2,142,82,18,64,24.7,0.761,21,Negative
+6,144,72,27,228,33.9,0.255,40,Negative
+2,92,62,28,0,31.6,0.13,24,Negative
+1,71,48,18,76,20.4,0.323,22,Negative
+6,93,50,30,64,28.7,0.356,23,Negative
+1,122,90,51,220,49.7,0.325,31,Positive
+1,163,72,0,0,39,1.222,33,Positive
+1,151,60,0,0,26.1,0.179,22,Negative
+0,125,96,0,0,22.5,0.262,21,Negative
+1,81,72,18,40,26.6,0.283,24,Negative
+2,85,65,0,0,39.6,0.93,27,Negative
+1,126,56,29,152,28.7,0.801,21,Negative
+1,96,122,0,0,22.4,0.207,27,Negative
+4,144,58,28,140,29.5,0.287,37,Negative
+3,83,58,31,18,34.3,0.336,25,Negative
+0,95,85,25,36,37.4,0.247,24,Positive
+3,171,72,33,135,33.3,0.199,24,Positive
+8,155,62,26,495,34,0.543,46,Positive
+1,89,76,34,37,31.2,0.192,23,Negative
+4,76,62,0,0,34,0.391,25,Negative
+7,160,54,32,175,30.5,0.588,39,Positive
+4,146,92,0,0,31.2,0.539,61,Positive
+5,124,74,0,0,34,0.22,38,Positive
+5,78,48,0,0,33.7,0.654,25,Negative
+4,97,60,23,0,28.2,0.443,22,Negative
+4,99,76,15,51,23.2,0.223,21,Negative
+0,162,76,56,100,53.2,0.759,25,Positive
+6,111,64,39,0,34.2,0.26,24,Negative
+2,107,74,30,100,33.6,0.404,23,Negative
+5,132,80,0,0,26.8,0.186,69,Negative
+0,113,76,0,0,33.3,0.278,23,Positive
+1,88,30,42,99,55,0.496,26,Positive
+3,120,70,30,135,42.9,0.452,30,Negative
+1,118,58,36,94,33.3,0.261,23,Negative
+1,117,88,24,145,34.5,0.403,40,Positive
+0,105,84,0,0,27.9,0.741,62,Positive
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+7,194,68,28,0,35.9,0.745,41,Positive
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+11,135,0,0,0,52.3,0.578,40,Positive
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+3,107,62,13,48,22.9,0.678,23,Positive
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+0,113,80,16,0,31,0.874,21,Negative
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+8,196,76,29,280,37.5,0.605,57,Positive
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+0,140,65,26,130,42.6,0.431,24,Positive
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+12,151,70,40,271,41.8,0.742,38,Positive
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+6,125,68,30,120,30,0.464,32,Negative
+5,85,74,22,0,29,1.224,32,Positive
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+3,162,52,38,0,37.2,0.652,24,Positive
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+4,142,86,0,0,44,0.645,22,Positive
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+4,171,72,0,0,43.6,0.479,26,Positive
+7,181,84,21,192,35.9,0.586,51,Positive
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+3,139,54,0,0,25.6,0.402,22,Positive
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+2,146,76,35,194,38.2,0.329,29,Negative
+9,184,85,15,0,30,1.213,49,Positive
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+12,92,62,7,258,27.6,0.926,44,Positive
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+11,155,76,28,150,33.3,1.353,51,Positive
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+3,141,0,0,0,30,0.761,27,Positive
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+4,123,62,0,0,32,0.226,35,Positive
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+2,128,64,42,0,40,1.101,24,Negative
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+7,106,60,24,0,26.5,0.296,29,Positive
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+0,146,70,0,0,37.9,0.334,28,Positive
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+7,161,86,0,0,30.4,0.165,47,Positive
+2,108,80,0,0,27,0.259,52,Positive
+7,136,74,26,135,26,0.647,51,Negative
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+1,119,86,39,220,45.6,0.808,29,Positive
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+0,107,62,30,74,36.6,0.757,25,Positive
+2,128,78,37,182,43.3,1.224,31,Positive
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+2,146,70,38,360,28,0.337,29,Positive
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+14,100,78,25,184,36.6,0.412,46,Positive
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+2,144,58,33,135,31.6,0.422,25,Positive
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+3,150,76,0,0,21,0.207,37,Negative
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+10,161,68,23,132,25.5,0.326,47,Positive
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+0,128,68,19,180,30.5,1.391,25,Positive
+2,124,68,28,205,32.9,0.875,30,Positive
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+2,155,74,17,96,26.6,0.433,27,Positive
+3,113,50,10,85,29.5,0.626,25,Negative
+7,109,80,31,0,35.9,1.127,43,Positive
+2,112,68,22,94,34.1,0.315,26,Negative
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+3,182,74,0,0,30.5,0.345,29,Positive
+3,115,66,39,140,38.1,0.15,28,Negative
+6,194,78,0,0,23.5,0.129,59,Positive
+4,129,60,12,231,27.5,0.527,31,Negative
+3,112,74,30,0,31.6,0.197,25,Positive
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+13,152,90,33,29,26.8,0.731,43,Positive
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+1,122,64,32,156,35.1,0.692,30,Positive
+10,179,70,0,0,35.1,0.2,37,Negative
+2,102,86,36,120,45.5,0.127,23,Positive
+6,105,70,32,68,30.8,0.122,37,Negative
+8,118,72,19,0,23.1,1.476,46,Negative
+2,87,58,16,52,32.7,0.166,25,Negative
+1,180,0,0,0,43.3,0.282,41,Positive
+12,106,80,0,0,23.6,0.137,44,Negative
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+0,117,0,0,0,33.8,0.932,44,Negative
+5,115,76,0,0,31.2,0.343,44,Positive
+9,152,78,34,171,34.2,0.893,33,Positive
+7,178,84,0,0,39.9,0.331,41,Positive
+1,130,70,13,105,25.9,0.472,22,Negative
+1,95,74,21,73,25.9,0.673,36,Negative
+1,0,68,35,0,32,0.389,22,Negative
+5,122,86,0,0,34.7,0.29,33,Negative
+8,95,72,0,0,36.8,0.485,57,Negative
+8,126,88,36,108,38.5,0.349,49,Negative
+1,139,46,19,83,28.7,0.654,22,Negative
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+3,99,62,19,74,21.8,0.279,26,Negative
+5,0,80,32,0,41,0.346,37,Positive
+4,92,80,0,0,42.2,0.237,29,Negative
+4,137,84,0,0,31.2,0.252,30,Negative
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+1,90,62,12,43,27.2,0.58,24,Negative
+3,90,78,0,0,42.7,0.559,21,Negative
+9,165,88,0,0,30.4,0.302,49,Positive
+1,125,50,40,167,33.3,0.962,28,Positive
+13,129,0,30,0,39.9,0.569,44,Positive
+12,88,74,40,54,35.3,0.378,48,Negative
+1,196,76,36,249,36.5,0.875,29,Positive
+5,189,64,33,325,31.2,0.583,29,Positive
+5,158,70,0,0,29.8,0.207,63,Negative
+5,103,108,37,0,39.2,0.305,65,Negative
+4,146,78,0,0,38.5,0.52,67,Positive
+4,147,74,25,293,34.9,0.385,30,Negative
+5,99,54,28,83,34,0.499,30,Negative
+6,124,72,0,0,27.6,0.368,29,Positive
+0,101,64,17,0,21,0.252,21,Negative
+3,81,86,16,66,27.5,0.306,22,Negative
+1,133,102,28,140,32.8,0.234,45,Positive
+3,173,82,48,465,38.4,2.137,25,Positive
+0,118,64,23,89,0,1.731,21,Negative
+0,84,64,22,66,35.8,0.545,21,Negative
+2,105,58,40,94,34.9,0.225,25,Negative
+2,122,52,43,158,36.2,0.816,28,Negative
+12,140,82,43,325,39.2,0.528,58,Positive
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+1,87,60,37,75,37.2,0.509,22,Negative
+4,156,75,0,0,48.3,0.238,32,Positive
+0,93,100,39,72,43.4,1.021,35,Negative
+1,107,72,30,82,30.8,0.821,24,Negative
+0,105,68,22,0,20,0.236,22,Negative
+1,109,60,8,182,25.4,0.947,21,Negative
+1,90,62,18,59,25.1,1.268,25,Negative
+1,125,70,24,110,24.3,0.221,25,Negative
+1,119,54,13,50,22.3,0.205,24,Negative
+5,116,74,29,0,32.3,0.66,35,Positive
+8,105,100,36,0,43.3,0.239,45,Positive
+5,144,82,26,285,32,0.452,58,Positive
+3,100,68,23,81,31.6,0.949,28,Negative
+1,100,66,29,196,32,0.444,42,Negative
+5,166,76,0,0,45.7,0.34,27,Positive
+1,131,64,14,415,23.7,0.389,21,Negative
+4,116,72,12,87,22.1,0.463,37,Negative
+4,158,78,0,0,32.9,0.803,31,Positive
+2,127,58,24,275,27.7,1.6,25,Negative
+3,96,56,34,115,24.7,0.944,39,Negative
+0,131,66,40,0,34.3,0.196,22,Positive
+3,82,70,0,0,21.1,0.389,25,Negative
+3,193,70,31,0,34.9,0.241,25,Positive
+4,95,64,0,0,32,0.161,31,Positive
+6,137,61,0,0,24.2,0.151,55,Negative
+5,136,84,41,88,35,0.286,35,Positive
+9,72,78,25,0,31.6,0.28,38,Negative
+5,168,64,0,0,32.9,0.135,41,Positive
+2,123,48,32,165,42.1,0.52,26,Negative
+4,115,72,0,0,28.9,0.376,46,Positive
+0,101,62,0,0,21.9,0.336,25,Negative
+8,197,74,0,0,25.9,1.191,39,Positive
+1,172,68,49,579,42.4,0.702,28,Positive
+6,102,90,39,0,35.7,0.674,28,Negative
+1,112,72,30,176,34.4,0.528,25,Negative
+1,143,84,23,310,42.4,1.076,22,Negative
+1,143,74,22,61,26.2,0.256,21,Negative
+0,138,60,35,167,34.6,0.534,21,Positive
+3,173,84,33,474,35.7,0.258,22,Positive
+1,97,68,21,0,27.2,1.095,22,Negative
+4,144,82,32,0,38.5,0.554,37,Positive
+1,83,68,0,0,18.2,0.624,27,Negative
+3,129,64,29,115,26.4,0.219,28,Positive
+1,119,88,41,170,45.3,0.507,26,Negative
+2,94,68,18,76,26,0.561,21,Negative
+0,102,64,46,78,40.6,0.496,21,Negative
+2,115,64,22,0,30.8,0.421,21,Negative
+8,151,78,32,210,42.9,0.516,36,Positive
+4,184,78,39,277,37,0.264,31,Positive
+0,94,0,0,0,0,0.256,25,Negative
+1,181,64,30,180,34.1,0.328,38,Positive
+0,135,94,46,145,40.6,0.284,26,Negative
+1,95,82,25,180,35,0.233,43,Positive
+2,99,0,0,0,22.2,0.108,23,Negative
+3,89,74,16,85,30.4,0.551,38,Negative
+1,80,74,11,60,30,0.527,22,Negative
+2,139,75,0,0,25.6,0.167,29,Negative
+1,90,68,8,0,24.5,1.138,36,Negative
+0,141,0,0,0,42.4,0.205,29,Positive
+12,140,85,33,0,37.4,0.244,41,Negative
+5,147,75,0,0,29.9,0.434,28,Negative
+1,97,70,15,0,18.2,0.147,21,Negative
+6,107,88,0,0,36.8,0.727,31,Negative
+0,189,104,25,0,34.3,0.435,41,Positive
+2,83,66,23,50,32.2,0.497,22,Negative
+4,117,64,27,120,33.2,0.23,24,Negative
+8,108,70,0,0,30.5,0.955,33,Positive
+4,117,62,12,0,29.7,0.38,30,Positive
+0,180,78,63,14,59.4,2.42,25,Positive
+1,100,72,12,70,25.3,0.658,28,Negative
+0,95,80,45,92,36.5,0.33,26,Negative
+0,104,64,37,64,33.6,0.51,22,Positive
+0,120,74,18,63,30.5,0.285,26,Negative
+1,82,64,13,95,21.2,0.415,23,Negative
+2,134,70,0,0,28.9,0.542,23,Positive
+0,91,68,32,210,39.9,0.381,25,Negative
+2,119,0,0,0,19.6,0.832,72,Negative
+2,100,54,28,105,37.8,0.498,24,Negative
+14,175,62,30,0,33.6,0.212,38,Positive
+1,135,54,0,0,26.7,0.687,62,Negative
+5,86,68,28,71,30.2,0.364,24,Negative
+10,148,84,48,237,37.6,1.001,51,Positive
+9,134,74,33,60,25.9,0.46,81,Negative
+9,120,72,22,56,20.8,0.733,48,Negative
+1,71,62,0,0,21.8,0.416,26,Negative
+8,74,70,40,49,35.3,0.705,39,Negative
+5,88,78,30,0,27.6,0.258,37,Negative
+10,115,98,0,0,24,1.022,34,Negative
+0,124,56,13,105,21.8,0.452,21,Negative
+0,74,52,10,36,27.8,0.269,22,Negative
+0,97,64,36,100,36.8,0.6,25,Negative
+8,120,0,0,0,30,0.183,38,Positive
+6,154,78,41,140,46.1,0.571,27,Negative
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frontend/package.json

@@ -11,27 +11,28 @@
     "start": "npm run dev"
   },
   "dependencies": {
-    "@babel/core": "^7.8.3",
+    "@babel/core": "^7.8.4",
-    "@babel/runtime": "^7.8.3",
+    "@babel/runtime": "^7.8.4",
-    "@fortawesome/fontawesome-free": "^5.12.0",
+    "@fortawesome/fontawesome-free": "^5.12.1",
-    "@fortawesome/fontawesome-svg-core": "^1.2.26",
+    "@fortawesome/fontawesome-svg-core": "^1.2.27",
-    "@fortawesome/free-solid-svg-icons": "^5.12.0",
+    "@fortawesome/free-solid-svg-icons": "^5.12.1",
     "@fortawesome/vue-fontawesome": "^0.1.9",
     "@statnett/vue-plotly": "^0.3.2",
     "@types/d3-drag": "^1.2.3",
-    "@types/node": "^13.5.1",
+    "@types/node": "^13.7.4",
-    "ajv": "^6.11.0",
+    "ajv": "^6.12.0",
     "audit": "0.0.6",
     "axios": "^0.19.2",
     "axios-progress-bar": "^1.2.0",
     "babel-preset-vue": "^2.0.2",
-    "blob": "0.0.5",
+    "blob": "0.1.0",
     "bootstrap": "^4.4.1",
     "bootstrap-toggle": "^2.2.2",
-    "bootstrap-vue": "^2.3.0",
+    "bootstrap-vue": "^2.5.0",
     "circlepack-chart": "^1.3.0",
     "clean-webpack-plugin": "^3.0.0",
     "colorbrewer": "^1.3.0",
+    "cryo": "0.0.6",
     "d3": "^5.15.0",
     "d3-array": "^2.4.0",
     "d3-brush": "^1.1.5",
@@ -50,15 +51,15 @@
     "fs": "0.0.2",
     "fs-es6": "0.0.2",
     "ify-loader": "^1.1.0",
-    "interactjs": "^1.8.2",
+    "interactjs": "^1.8.4",
     "jquery": "^3.4.1",
-    "mdbvue": "^6.3.0",
+    "mdbvue": "^6.5.0",
     "merge": "^1.2.1",
     "mini-css-extract-plugin": "^0.9.0",
     "npm-check-updates": "^4.0.1",
     "papaparse": "^5.1.1",
     "parcoord-es": "^2.2.10",
-    "plotly.js": "^1.52.1",
+    "plotly.js": "^1.52.2",
     "popper.js": "^1.16.1",
     "react": "^16.12.0",
     "react-dom": "^16.12.0",
@@ -75,9 +76,9 @@
     "vue-papa-parse": "^1.3.0",
     "vue-plotly": "^1.1.0",
     "vue-router": "^3.1.5",
-    "vue-slider-component": "^3.1.0",
+    "vue-slider-component": "^3.1.1",
     "vue2-simplert-plugin": "^0.5.3",
-    "webpack-cli": "^3.3.10",
+    "webpack-cli": "^3.3.11",
     "webpack-require": "0.0.16"
   },
   "devDependencies": {
@@ -92,7 +93,7 @@
     "@babel/plugin-syntax-import-meta": "^7.8.3",
     "@babel/plugin-syntax-jsx": "^7.8.3",
     "@babel/plugin-transform-runtime": "^7.8.3",
-    "@babel/preset-env": "^7.8.3",
+    "@babel/preset-env": "^7.8.4",
     "autoprefixer": "^9.7.4",
     "babel-eslint": "^10.0.3",
     "babel-helper-vue-jsx-merge-props": "^2.0.3",
@@ -105,13 +106,13 @@
     "eslint-config-standard": "^14.1.0",
     "eslint-friendly-formatter": "^4.0.1",
     "eslint-loader": "^3.0.3",
-    "eslint-plugin-import": "^2.20.0",
+    "eslint-plugin-import": "^2.20.1",
     "eslint-plugin-node": "^11.0.0",
     "eslint-plugin-promise": "^4.2.1",
     "eslint-plugin-standard": "^4.0.1",
-    "eslint-plugin-vue": "^6.1.2",
+    "eslint-plugin-vue": "^6.2.1",
     "extract-text-webpack-plugin": "^3.0.2",
-    "file-loader": "^5.0.2",
+    "file-loader": "^5.1.0",
     "friendly-errors-webpack-plugin": "^1.7.0",
     "html-webpack-plugin": "^3.2.0",
     "node-notifier": "^6.0.0",
@@ -121,24 +122,24 @@
     "postcss-import": "^12.0.1",
     "postcss-loader": "^3.0.0",
     "postcss-url": "^8.0.0",
-    "rimraf": "^3.0.1",
+    "rimraf": "^3.0.2",
     "sass": "^1.25.0",
     "sass-loader": "^8.0.2",
-    "semver": "^7.1.1",
+    "semver": "^7.1.3",
     "shelljs": "^0.8.3",
     "uglifyjs-webpack-plugin": "^2.2.0",
     "url-loader": "^3.0.0",
-    "vue-class-component": "^7.2.2",
+    "vue-class-component": "^7.2.3",
-    "vue-cli-plugin-vuetify": "^2.0.3",
+    "vue-cli-plugin-vuetify": "^2.0.5",
-    "vue-loader": "^15.8.3",
+    "vue-loader": "^15.9.0",
-    "vue-property-decorator": "^8.3.0",
+    "vue-property-decorator": "^8.4.0",
     "vue-style-loader": "^4.1.2",
     "vue-template-compiler": "^2.6.11",
     "vue2-simplert": "^1.0.0",
     "vuetify-loader": "^1.4.3",
-    "webpack": "^4.41.5",
+    "webpack": "^4.41.6",
     "webpack-bundle-analyzer": "^3.6.0",
-    "webpack-dev-server": "^3.10.1",
+    "webpack-dev-server": "^3.10.3",
     "webpack-merge": "^4.2.2"
   },
   "browserslist": [

frontend/src/components/AlgorithmHyperParam.vue

@@ -65,13 +65,10 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgKNN['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgKNN['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgKNN['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgKNN['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgKNN['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgKNN['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgKNN['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgKNN['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgKNN['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgKNN['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgKNN['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgKNN['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgKNN['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgKNN['log_loss'])[j]))
-          if (sumKNN <= 0) {
-            sumKNN = 0
-          }
           McKNN.push((sumKNN/divide)*100)
         }
         var McSVC = []
-        const performanceAlgSVC = JSON.parse(this.ModelsPerformance[14])
+        const performanceAlgSVC = JSON.parse(this.ModelsPerformance[15])
         for (let j = 0; j < Object.values(performanceAlgSVC['mean_test_accuracy']).length; j++) {
           let sumSVC
           sumSVC = (factorsLocal[0] * Object.values(performanceAlgSVC['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgSVC['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgSVC['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgSVC['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgSVC['geometric_mean_score_macro'])[j])
@@ -79,13 +76,10 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgSVC['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgSVC['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgSVC['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgSVC['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgSVC['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgSVC['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgSVC['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgSVC['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgSVC['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgSVC['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgSVC['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgSVC['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgSVC['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgSVC['log_loss'])[j]))
-          if (sumSVC <= 0) {
-            sumSVC = 0
-          }
           McSVC.push((sumSVC/divide)*100)
         }
         var McGausNB = []
-        const performanceAlgGausNB = JSON.parse(this.ModelsPerformance[22])
+        const performanceAlgGausNB = JSON.parse(this.ModelsPerformance[24])
         for (let j = 0; j < Object.values(performanceAlgGausNB['mean_test_accuracy']).length; j++) {
           let sumGausNB 
           sumGausNB = (factorsLocal[0] * Object.values(performanceAlgGausNB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgGausNB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgGausNB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgGausNB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgGausNB['geometric_mean_score_macro'])[j])
@@ -93,13 +87,10 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgGausNB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGausNB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGausNB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGausNB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGausNB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGausNB['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgGausNB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGausNB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGausNB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGausNB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGausNB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGausNB['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgGausNB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGausNB['log_loss'])[j]))
-          if (sumGausNB <= 0) {
-            sumGausNB = 0
-          }
           McGausNB.push((sumGausNB/divide)*100)
         }
         var McMLP = []
-        const performanceAlgMLP = JSON.parse(this.ModelsPerformance[30])
+        const performanceAlgMLP = JSON.parse(this.ModelsPerformance[33])
         for (let j = 0; j < Object.values(performanceAlgMLP['mean_test_accuracy']).length; j++) {
           let sumMLP
           sumMLP = (factorsLocal[0] * Object.values(performanceAlgMLP['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgMLP['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgMLP['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgMLP['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgMLP['geometric_mean_score_macro'])[j])
@@ -107,13 +98,10 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgMLP['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgMLP['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgMLP['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgMLP['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgMLP['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgMLP['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgMLP['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgMLP['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgMLP['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgMLP['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgMLP['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgMLP['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgMLP['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgMLP['log_loss'])[j]))
-          if (sumMLP <= 0) {
-            sumMLP = 0
-          }
           McMLP.push((sumMLP/divide)*100)
         }
         var McLR = []
-        const performanceAlgLR = JSON.parse(this.ModelsPerformance[38])
+        const performanceAlgLR = JSON.parse(this.ModelsPerformance[42])
         for (let j = 0; j < Object.values(performanceAlgLR['mean_test_accuracy']).length; j++) {
           let sumLR
           sumLR = (factorsLocal[0] * Object.values(performanceAlgLR['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgLR['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgLR['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgLR['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgLR['geometric_mean_score_macro'])[j])
@@ -121,13 +109,10 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgLR['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLR['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLR['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLR['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLR['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLR['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgLR['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLR['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLR['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLR['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLR['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLR['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgLR['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLR['log_loss'])[j]))
-          if (sumLR <= 0) {
-            sumLR = 0
-          }
           McLR.push((sumLR/divide)*100)
         }
         var McLDA = []
-        const performanceAlgLDA = JSON.parse(this.ModelsPerformance[46])
+        const performanceAlgLDA = JSON.parse(this.ModelsPerformance[51])
         for (let j = 0; j < Object.values(performanceAlgLDA['mean_test_accuracy']).length; j++) {
           let sumLDA
           sumLDA = (factorsLocal[0] * Object.values(performanceAlgLDA['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgLDA['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgLDA['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgLDA['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgLDA['geometric_mean_score_macro'])[j])
@@ -135,13 +120,10 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgLDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLDA['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgLDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLDA['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgLDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLDA['log_loss'])[j]))
-          if (sumLDA <= 0) {
-            sumLDA = 0
-          }
           McLDA.push((sumLDA/divide)*100)
         }
         var McQDA = []
-        const performanceAlgQDA = JSON.parse(this.ModelsPerformance[54])
+        const performanceAlgQDA = JSON.parse(this.ModelsPerformance[60])
         for (let j = 0; j < Object.values(performanceAlgQDA['mean_test_accuracy']).length; j++) {
           let sumQDA
           sumQDA = (factorsLocal[0] * Object.values(performanceAlgQDA['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgQDA['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgQDA['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgQDA['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgQDA['geometric_mean_score_macro'])[j])
@@ -149,13 +131,10 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgQDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgQDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgQDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgQDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgQDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgQDA['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgQDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgQDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgQDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgQDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgQDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgQDA['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgQDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgQDA['log_loss'])[j]))
-          if (sumQDA <= 0) {
-            sumQDA = 0
-          }
           McQDA.push((sumQDA/divide)*100)
         }
         var McRF = []
-        const performanceAlgRF = JSON.parse(this.ModelsPerformance[62])
+        const performanceAlgRF = JSON.parse(this.ModelsPerformance[69])
         for (let j = 0; j < Object.values(performanceAlgRF['mean_test_accuracy']).length; j++) {
           let sumRF
           sumRF = (factorsLocal[0] * Object.values(performanceAlgRF['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgRF['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgRF['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgRF['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgRF['geometric_mean_score_macro'])[j])
@@ -163,13 +142,10 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgRF['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgRF['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgRF['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgRF['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgRF['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgRF['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgRF['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgRF['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgRF['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgRF['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgRF['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgRF['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgRF['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgRF['log_loss'])[j]))
-          if (sumRF <= 0) {
-            sumRF = 0
-          }
           McRF.push((sumRF/divide)*100)
         }
         var McExtraT = []
-        const performanceAlgExtraT = JSON.parse(this.ModelsPerformance[70])
+        const performanceAlgExtraT = JSON.parse(this.ModelsPerformance[78])
         for (let j = 0; j < Object.values(performanceAlgExtraT['mean_test_accuracy']).length; j++) {
           let sumExtraT
           sumExtraT = (factorsLocal[0] * Object.values(performanceAlgExtraT['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgExtraT['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgExtraT['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgExtraT['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgExtraT['geometric_mean_score_macro'])[j])
@@ -177,13 +153,10 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgExtraT['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgExtraT['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgExtraT['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgExtraT['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgExtraT['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgExtraT['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgExtraT['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgExtraT['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgExtraT['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgExtraT['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgExtraT['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgExtraT['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgExtraT['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgExtraT['log_loss'])[j]))
-          if (sumExtraT <= 0) {
-            sumExtraT = 0
-          }
           McExtraT.push((sumExtraT/divide)*100)
         }
         var McAdaB = []
-        const performanceAlgAdaB = JSON.parse(this.ModelsPerformance[78])
+        const performanceAlgAdaB = JSON.parse(this.ModelsPerformance[87])
         for (let j = 0; j < Object.values(performanceAlgAdaB['mean_test_accuracy']).length; j++) {
           let sumAdaB
           sumAdaB = (factorsLocal[0] * Object.values(performanceAlgAdaB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgAdaB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgAdaB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgAdaB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgAdaB['geometric_mean_score_macro'])[j])
@@ -191,13 +164,10 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgAdaB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgAdaB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgAdaB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgAdaB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgAdaB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgAdaB['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgAdaB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgAdaB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgAdaB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgAdaB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgAdaB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgAdaB['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgAdaB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgAdaB['log_loss'])[j]))
-          if (sumAdaB <= 0) {
-            sumAdaB = 0
-          }
           McAdaB.push((sumAdaB/divide)*100)
         }
         var McGradB = []
-        const performanceAlgGradB = JSON.parse(this.ModelsPerformance[86])
+        const performanceAlgGradB = JSON.parse(this.ModelsPerformance[96])
         for (let j = 0; j < Object.values(performanceAlgGradB['mean_test_accuracy']).length; j++) {
           let sumGradB
           sumGradB = (factorsLocal[0] * Object.values(performanceAlgGradB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgGradB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgGradB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgGradB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgGradB['geometric_mean_score_macro'])[j])
@@ -205,9 +175,6 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgGradB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGradB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGradB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGradB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGradB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGradB['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgGradB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGradB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGradB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGradB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGradB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGradB['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgGradB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGradB['log_loss'])[j]))
-          if (sumGradB <= 0) {
-            sumGradB = 0
-          }
           McGradB.push((sumGradB/divide)*100)
         }
 
@@ -216,34 +183,34 @@ export default {
           Combined = JSON.parse(this.ModelsPerformance[1])
           colorGiv = colors[0]
         } else if (this.selAlgorithm == 'SVC') {    
-          Combined = JSON.parse(this.ModelsPerformance[9])
+          Combined = JSON.parse(this.ModelsPerformance[10])
           colorGiv = colors[1]
         } else if (this.selAlgorithm == 'GausNB') {    
-          Combined = JSON.parse(this.ModelsPerformance[17])
+          Combined = JSON.parse(this.ModelsPerformance[19])
           colorGiv = colors[2]
         } else if (this.selAlgorithm == 'MLP') {    
-          Combined = JSON.parse(this.ModelsPerformance[25])
+          Combined = JSON.parse(this.ModelsPerformance[28])
           colorGiv = colors[3]
         } else if (this.selAlgorithm == 'LR') {    
-          Combined = JSON.parse(this.ModelsPerformance[33])
+          Combined = JSON.parse(this.ModelsPerformance[37])
           colorGiv = colors[4]
         } else if (this.selAlgorithm == 'LDA') {    
-          Combined = JSON.parse(this.ModelsPerformance[41])
+          Combined = JSON.parse(this.ModelsPerformance[46])
           colorGiv = colors[5]
         } else if (this.selAlgorithm == 'QDA') {    
-          Combined = JSON.parse(this.ModelsPerformance[49])
+          Combined = JSON.parse(this.ModelsPerformance[55])
           colorGiv = colors[6]
         } else if (this.selAlgorithm == 'RF') {    
-          Combined = JSON.parse(this.ModelsPerformance[57])
+          Combined = JSON.parse(this.ModelsPerformance[64])
           colorGiv = colors[7]
         } else if (this.selAlgorithm == 'ExtraT') {    
-          Combined = JSON.parse(this.ModelsPerformance[65])
+          Combined = JSON.parse(this.ModelsPerformance[73])
           colorGiv = colors[8]
         } else if (this.selAlgorithm == 'AdaB') {    
-          Combined = JSON.parse(this.ModelsPerformance[73])
+          Combined = JSON.parse(this.ModelsPerformance[82])
           colorGiv = colors[9]
         } else {
-          Combined = JSON.parse(this.ModelsPerformance[81])
+          Combined = JSON.parse(this.ModelsPerformance[91])
           colorGiv = colors[10]
         }
         var valuesPerf = Object.values(Combined['params'])

frontend/src/components/Algorithms.vue

@@ -39,6 +39,7 @@ export default {
       parameters: [],
       algorithm1: [],
       algorithm2: [],
+      activeTabVal: true,
       factors: [1,1,1,0,0
       ,1,0,0,1,0
       ,0,1,0,0,0
@@ -60,16 +61,16 @@ export default {
 
       // retrieve models ID
       const AlgorKNNIDs = this.PerformanceAllModels[0]
-      const AlgorSVCIDs = this.PerformanceAllModels[8]
+      const AlgorSVCIDs = this.PerformanceAllModels[9]
-      const AlgorGausNBIDs = this.PerformanceAllModels[16]
+      const AlgorGausNBIDs = this.PerformanceAllModels[18]
-      const AlgorMLPIDs = this.PerformanceAllModels[24]
+      const AlgorMLPIDs = this.PerformanceAllModels[27]
-      const AlgorLRIDs = this.PerformanceAllModels[32]
+      const AlgorLRIDs = this.PerformanceAllModels[36]
-      const AlgorLDAIDs = this.PerformanceAllModels[40]
+      const AlgorLDAIDs = this.PerformanceAllModels[45]
-      const AlgorQDAIDs = this.PerformanceAllModels[48]
+      const AlgorQDAIDs = this.PerformanceAllModels[54]
-      const AlgorRFIDs = this.PerformanceAllModels[56]
+      const AlgorRFIDs = this.PerformanceAllModels[63]
-      const AlgorExtraTIDs = this.PerformanceAllModels[64]
+      const AlgorExtraTIDs = this.PerformanceAllModels[72]
-      const AlgorAdaBIDs = this.PerformanceAllModels[72]
+      const AlgorAdaBIDs = this.PerformanceAllModels[81]
-      const AlgorGradBIDs = this.PerformanceAllModels[80]
+      const AlgorGradBIDs = this.PerformanceAllModels[90]
 
       var factorsLocal = this.factors
       var divide = 0
@@ -87,13 +88,10 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgKNN['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgKNN['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgKNN['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgKNN['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgKNN['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgKNN['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgKNN['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgKNN['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgKNN['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgKNN['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgKNN['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgKNN['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgKNN['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgKNN['log_loss'])[j]))
-        if (sumKNN <= 0) {
-          sumKNN = 0
-        }
         McKNN.push((sumKNN/divide)*100)
       }
       var McSVC = []
-      const performanceAlgSVC = JSON.parse(this.PerformanceAllModels[14])
+      const performanceAlgSVC = JSON.parse(this.PerformanceAllModels[15])
       for (let j = 0; j < Object.values(performanceAlgSVC['mean_test_accuracy']).length; j++) {
         let sumSVC
         sumSVC = (factorsLocal[0] * Object.values(performanceAlgSVC['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgSVC['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgSVC['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgSVC['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgSVC['geometric_mean_score_macro'])[j])
@@ -101,13 +99,11 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgSVC['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgSVC['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgSVC['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgSVC['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgSVC['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgSVC['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgSVC['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgSVC['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgSVC['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgSVC['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgSVC['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgSVC['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgSVC['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgSVC['log_loss'])[j]))
-        if (sumSVC <= 0) {
+
-          sumSVC = 0
-        }
         McSVC.push((sumSVC/divide)*100)
       }
       var McGausNB = []
-      const performanceAlgGausNB = JSON.parse(this.PerformanceAllModels[22])
+      const performanceAlgGausNB = JSON.parse(this.PerformanceAllModels[24])
       for (let j = 0; j < Object.values(performanceAlgGausNB['mean_test_accuracy']).length; j++) {
         let sumGausNB 
         sumGausNB = (factorsLocal[0] * Object.values(performanceAlgGausNB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgGausNB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgGausNB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgGausNB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgGausNB['geometric_mean_score_macro'])[j])
@@ -115,13 +111,10 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgGausNB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGausNB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGausNB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGausNB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGausNB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGausNB['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgGausNB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGausNB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGausNB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGausNB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGausNB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGausNB['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgGausNB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGausNB['log_loss'])[j]))
-        if (sumGausNB <= 0) {
-          sumGausNB = 0
-        }
         McGausNB.push((sumGausNB/divide)*100)
       }
       var McMLP = []
-      const performanceAlgMLP = JSON.parse(this.PerformanceAllModels[30])
+      const performanceAlgMLP = JSON.parse(this.PerformanceAllModels[33])
       for (let j = 0; j < Object.values(performanceAlgMLP['mean_test_accuracy']).length; j++) {
         let sumMLP
         sumMLP = (factorsLocal[0] * Object.values(performanceAlgMLP['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgMLP['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgMLP['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgMLP['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgMLP['geometric_mean_score_macro'])[j])
@@ -129,13 +122,10 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgMLP['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgMLP['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgMLP['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgMLP['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgMLP['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgMLP['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgMLP['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgMLP['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgMLP['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgMLP['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgMLP['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgMLP['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgMLP['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgMLP['log_loss'])[j]))
-      if (sumMLP <= 0) {
-          sumMLP = 0
-        }
       McMLP.push((sumMLP/divide)*100)
       }
       var McLR = []
-      const performanceAlgLR = JSON.parse(this.PerformanceAllModels[38])
+      const performanceAlgLR = JSON.parse(this.PerformanceAllModels[42])
       for (let j = 0; j < Object.values(performanceAlgLR['mean_test_accuracy']).length; j++) {
         let sumLR
         sumLR = (factorsLocal[0] * Object.values(performanceAlgLR['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgLR['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgLR['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgLR['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgLR['geometric_mean_score_macro'])[j])
@@ -143,13 +133,10 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgLR['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLR['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLR['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLR['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLR['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLR['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgLR['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLR['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLR['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLR['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLR['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLR['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgLR['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLR['log_loss'])[j]))
-      if (sumLR <= 0) {
-          sumLR = 0
-        }
       McLR.push((sumLR/divide)*100)
       }
       var McLDA = []
-      const performanceAlgLDA = JSON.parse(this.PerformanceAllModels[46])
+      const performanceAlgLDA = JSON.parse(this.PerformanceAllModels[51])
       for (let j = 0; j < Object.values(performanceAlgLDA['mean_test_accuracy']).length; j++) {
         let sumLDA
         sumLDA = (factorsLocal[0] * Object.values(performanceAlgLDA['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgLDA['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgLDA['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgLDA['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgLDA['geometric_mean_score_macro'])[j])
@@ -157,13 +144,10 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgLDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLDA['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgLDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLDA['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgLDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLDA['log_loss'])[j]))
-        if (sumLDA <= 0) {
-          sumLDA = 0
-        }
         McLDA.push((sumLDA/divide)*100)
       }
       var McQDA = []
-      const performanceAlgQDA = JSON.parse(this.PerformanceAllModels[54])
+      const performanceAlgQDA = JSON.parse(this.PerformanceAllModels[60])
       for (let j = 0; j < Object.values(performanceAlgQDA['mean_test_accuracy']).length; j++) {
         let sumQDA
         sumQDA = (factorsLocal[0] * Object.values(performanceAlgQDA['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgQDA['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgQDA['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgQDA['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgQDA['geometric_mean_score_macro'])[j])
@@ -171,13 +155,10 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgQDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgQDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgQDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgQDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgQDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgQDA['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgQDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgQDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgQDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgQDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgQDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgQDA['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgQDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgQDA['log_loss'])[j]))
-        if (sumQDA <= 0) {
-          sumQDA = 0
-        }
         McQDA.push((sumQDA/divide)*100)
       }
       var McRF = []
-      const performanceAlgRF = JSON.parse(this.PerformanceAllModels[62])
+      const performanceAlgRF = JSON.parse(this.PerformanceAllModels[69])
       for (let j = 0; j < Object.values(performanceAlgRF['mean_test_accuracy']).length; j++) {
         let sumRF
         sumRF = (factorsLocal[0] * Object.values(performanceAlgRF['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgRF['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgRF['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgRF['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgRF['geometric_mean_score_macro'])[j])
@@ -185,13 +166,10 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgRF['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgRF['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgRF['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgRF['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgRF['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgRF['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgRF['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgRF['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgRF['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgRF['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgRF['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgRF['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgRF['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgRF['log_loss'])[j]))
-        if (sumRF <= 0) {
-          sumRF = 0
-        }
         McRF.push((sumRF/divide)*100)
       }
       var McExtraT = []
-      const performanceAlgExtraT = JSON.parse(this.PerformanceAllModels[70])
+      const performanceAlgExtraT = JSON.parse(this.PerformanceAllModels[78])
       for (let j = 0; j < Object.values(performanceAlgExtraT['mean_test_accuracy']).length; j++) {
         let sumExtraT
         sumExtraT = (factorsLocal[0] * Object.values(performanceAlgExtraT['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgExtraT['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgExtraT['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgExtraT['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgExtraT['geometric_mean_score_macro'])[j])
@@ -199,13 +177,10 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgExtraT['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgExtraT['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgExtraT['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgExtraT['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgExtraT['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgExtraT['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgExtraT['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgExtraT['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgExtraT['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgExtraT['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgExtraT['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgExtraT['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgExtraT['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgExtraT['log_loss'])[j]))
-        if (sumExtraT <= 0) {
-          sumExtraT = 0
-        }
         McExtraT.push((sumExtraT/divide)*100)
       }
       var McAdaB = []
-      const performanceAlgAdaB = JSON.parse(this.PerformanceAllModels[78])
+      const performanceAlgAdaB = JSON.parse(this.PerformanceAllModels[87])
       for (let j = 0; j < Object.values(performanceAlgAdaB['mean_test_accuracy']).length; j++) {
         let sumAdaB
         sumAdaB = (factorsLocal[0] * Object.values(performanceAlgAdaB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgAdaB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgAdaB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgAdaB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgAdaB['geometric_mean_score_macro'])[j])
@@ -219,7 +194,7 @@ export default {
         McAdaB.push((sumAdaB/divide)*100)
       }
       var McGradB = []
-      const performanceAlgGradB = JSON.parse(this.PerformanceAllModels[86])
+      const performanceAlgGradB = JSON.parse(this.PerformanceAllModels[96])
       for (let j = 0; j < Object.values(performanceAlgGradB['mean_test_accuracy']).length; j++) {
         let sumGradB
         sumGradB = (factorsLocal[0] * Object.values(performanceAlgGradB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgGradB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgGradB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgGradB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgGradB['geometric_mean_score_macro'])[j])
@@ -227,24 +202,21 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgGradB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGradB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGradB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGradB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGradB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGradB['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgGradB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGradB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGradB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGradB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGradB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGradB['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgGradB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGradB['log_loss'])[j]))
-        if (sumGradB <= 0) {
-          sumGradB = 0
-        }
         McGradB.push((sumGradB/divide)*100)
       }
       
       // retrieve the results like performance
       const PerformAlgorKNN = JSON.parse(this.PerformanceAllModels[1])
-      const PerformAlgorSVC = JSON.parse(this.PerformanceAllModels[9])
+      const PerformAlgorSVC = JSON.parse(this.PerformanceAllModels[10])
-      const PerformAlgorGausNB = JSON.parse(this.PerformanceAllModels[17])
+      const PerformAlgorGausNB = JSON.parse(this.PerformanceAllModels[19])
-      const PerformAlgorMLP = JSON.parse(this.PerformanceAllModels[25])
+      const PerformAlgorMLP = JSON.parse(this.PerformanceAllModels[28])
-      const PerformAlgorLR = JSON.parse(this.PerformanceAllModels[33])
+      const PerformAlgorLR = JSON.parse(this.PerformanceAllModels[37])
-      const PerformAlgorLDA = JSON.parse(this.PerformanceAllModels[41])
+      const PerformAlgorLDA = JSON.parse(this.PerformanceAllModels[46])
-      const PerformAlgorQDA = JSON.parse(this.PerformanceAllModels[49])
+      const PerformAlgorQDA = JSON.parse(this.PerformanceAllModels[55])
-      const PerformAlgorRF = JSON.parse(this.PerformanceAllModels[57])
+      const PerformAlgorRF = JSON.parse(this.PerformanceAllModels[64])
-      const PerformAlgorExtraT = JSON.parse(this.PerformanceAllModels[65])
+      const PerformAlgorExtraT = JSON.parse(this.PerformanceAllModels[73])
-      const PerformAlgorAdaB = JSON.parse(this.PerformanceAllModels[73])
+      const PerformAlgorAdaB = JSON.parse(this.PerformanceAllModels[82])
-      const PerformAlgorGradB = JSON.parse(this.PerformanceAllModels[81])
+      const PerformAlgorGradB = JSON.parse(this.PerformanceAllModels[91])
 
       // initialize/instansiate algorithms and parameters
       this.algorithmKNN = []
@@ -265,7 +237,7 @@ export default {
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorKNN['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorSVC['params']).length; j++) {
-          this.algorithmSVC.push({'# Performance (%) #': McSVC[j],Algorithm:'C-Support Vector Classification',Model:'Model ' + AlgorSVCIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'; # Performance (%) # ',ModelID:AlgorSVCIDs[j]})
+          this.algorithmSVC.push({'# Performance (%) #': McSVC[j],Algorithm:'C-Support Vector Classif',Model:'Model ' + AlgorSVCIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'; # Performance (%) # ',ModelID:AlgorSVCIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorSVC['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorGausNB['params']).length; j++) {
@@ -310,7 +282,7 @@ export default {
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorKNN['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorSVC['params']).length; j++) {
-          this.algorithmSVC.push({'# Performance (%) #': this.listClassPerf[1][j],Algorithm:'C-Support Vector Classification',Model:'Model ' + AlgorSVCIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'; # Performance (%) # ',ModelID:AlgorSVCIDs[j]})
+          this.algorithmSVC.push({'# Performance (%) #': this.listClassPerf[1][j],Algorithm:'C-Support Vector Classif',Model:'Model ' + AlgorSVCIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'; # Performance (%) # ',ModelID:AlgorSVCIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorSVC['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorGausNB['params']).length; j++) {
@@ -413,7 +385,7 @@ export default {
         EventBus.$emit('updateBarChart', [])
       }
       el[1].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classification')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classif')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[1]
           allPoints[i].style.opacity = '1.0'
@@ -609,10 +581,10 @@ export default {
         EventBus.$emit('alternateFlagLock')
         EventBus.$emit('updateBarChart', [])
       }
-
-      // check if brushed through all boxplots and not only one at a time
       const myObserver = new ResizeObserver(entries => {
-        EventBus.$emit('brusheAllOn')
+        if (this.activeTabVal) {
+          (EventBus.$emit('brusheAllOn'))
+        }
       })
       var brushRect = document.querySelector('.extent')
       myObserver.observe(brushRect);
@@ -630,7 +602,7 @@ export default {
           var allPoints = document.getElementsByClassName('d3-exploding-boxplot point K-Nearest Neighbors')
           algorithm = this.algorithmKNN
         } else if (this.AllAlgorithms[j] === 'SVC') {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classification')
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classif')
           algorithm = this.algorithmSVC
         } else if (this.AllAlgorithms[j] === 'GausNB') {
           var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gaussian Naive Bayes')
@@ -805,7 +777,7 @@ export default {
       if (this.selectedAlgorithm === 'KNN') {
         var allPoints = document.getElementsByClassName('d3-exploding-boxplot point K-Nearest Neighbors')
       } else if (this.selectedAlgorithm === 'SVC') {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classification')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classif')
       } else if (this.selectedAlgorithm === 'GausNB') {
         var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gaussian Naive Bayes')
       } else if (this.selectedAlgorithm === 'MLP') {
@@ -975,7 +947,7 @@ export default {
           activeModels.push(allPoints[i].__data__.Model)
           if (allPoints[i].__data__.Algorithm === 'K-Nearest Neighbors') {
             algorithmsSelected.push('KNN')
-          } else if (allPoints[i].__data__.Algorithm === 'C-Support Vector Classification') {
+          } else if (allPoints[i].__data__.Algorithm === 'C-Support Vector Classif') {
             algorithmsSelected.push('SVC')
           } else if (allPoints[i].__data__.Algorithm === 'Gaussian Naive Bayes') {
             algorithmsSelected.push('GausNB')
@@ -1019,7 +991,7 @@ export default {
           activeModels.push(allPoints[i].__data__.Model)
           if (allPoints[i].__data__.Algorithm === 'K-Nearest Neighbors') {
             algorithmsSelected.push('KNN')
-          } else if (allPoints[i].__data__.Algorithm === 'C-Support Vector Classification') {
+          } else if (allPoints[i].__data__.Algorithm === 'C-Support Vector Classif') {
             algorithmsSelected.push('SVC')
           } else if (allPoints[i].__data__.Algorithm === 'Gaussian Naive Bayes') {
             algorithmsSelected.push('GausNB')
@@ -1086,6 +1058,8 @@ export default {
     },
   },
   mounted () {
+    EventBus.$on('Algorithm', data => { this.activeTabVal = data })
+
     EventBus.$on('emittedEventCallingModelBrushed', this.selectedPointsPerAlgorithm)
     EventBus.$on('emittedEventCallingAllAlgorithms', data => {
       this.PerformanceAllModels = data})

frontend/src/components/BalancePredictions.vue

@@ -46,7 +46,7 @@
             performancePerModel.forEach(element => {
               let el = {}
               el.type = "variable 1"
-              el.value = element * 100
+              el.value = element
               data.push(el)
             })
 
@@ -54,14 +54,14 @@
               performancePerModel.forEach(element => {
                 let el = {}
                 el.type = "variable 2"
-                el.value = element * 100
+                el.value = element
                 data.push(el)
               })
             } else {
               performancePerModelSelection.forEach(element => {
                 let el = {}
                 el.type = "variable 2"
-                el.value = element * 100
+                el.value = element
                 data.push(el)
               })
             }
@@ -92,7 +92,7 @@
             var histogram = d3.histogram()
                 .value(function(d) { return +d.value; })   // I need to give the vector of value
                 .domain(x.domain())  // then the domain of the graphic
-                .thresholds(x.ticks(40)); // then the numbers of bins
+                .thresholds(x.ticks(10)); // then the numbers of bins
 
             // And apply twice this function to data to get the bins.
             var bins1 = histogram(data.filter( function(d){return d.type === "variable 1"} ));
@@ -104,7 +104,7 @@
                 .domain([0, d3.max(bins1, function(d) { return d.length; })]);   // d3.hist has to be called before the Y axis obviously
             svg.append("g")
                 .attr("transform", "translate(-20,0)")
-                .call(d3.axisLeft(y1));
+                .call(d3.axisLeft(y1).ticks(5).tickSizeOuter(0));
 
             // Y axis: scale and draw:
             var y2 = d3.scaleLinear()
@@ -112,7 +112,7 @@
                 .domain([0, d3.max(bins2, function(d) { return d.length; })]);   // d3.hist has to be called before the Y axis obviously
             svg.append("g")
                 .attr("transform", "translate(-20,0)")
-                .call(d3.axisLeft(y2));
+                .call(d3.axisLeft(y2).ticks(5).tickSizeOuter(0));
 
              // Add a tooltip div. Here I define the general feature of the tooltip: stuff that do not depend on the data point.
             // Its opacity is set to 0: we don't see it by default.

frontend/src/components/BarChart.vue

@@ -25,6 +25,7 @@ export default {
       ,0,1,1,1
       ],
       SVCModels: 576,
+      tNameAll: '',
       GausNBModels: 736, 
       MLPModels: 1236,
       LRModels: 1356, 
@@ -41,16 +42,16 @@ export default {
   methods: {
     BarChartView () {
       const PerClassMetricsKNN = JSON.parse(this.PerformanceResults[2])
-      const PerClassMetricsSVC = JSON.parse(this.PerformanceResults[10])
+      const PerClassMetricsSVC = JSON.parse(this.PerformanceResults[11])
-      const PerClassMetricsGausNB = JSON.parse(this.PerformanceResults[18])
+      const PerClassMetricsGausNB = JSON.parse(this.PerformanceResults[20])
-      const PerClassMetricsMLP = JSON.parse(this.PerformanceResults[26])
+      const PerClassMetricsMLP = JSON.parse(this.PerformanceResults[29])
-      const PerClassMetricsLR = JSON.parse(this.PerformanceResults[34])
+      const PerClassMetricsLR = JSON.parse(this.PerformanceResults[38])
-      const PerClassMetricsLDA = JSON.parse(this.PerformanceResults[42])
+      const PerClassMetricsLDA = JSON.parse(this.PerformanceResults[47])
-      const PerClassMetricsQDA = JSON.parse(this.PerformanceResults[50])
+      const PerClassMetricsQDA = JSON.parse(this.PerformanceResults[56])
-      const PerClassMetricsRF = JSON.parse(this.PerformanceResults[58])
+      const PerClassMetricsRF = JSON.parse(this.PerformanceResults[65])
-      const PerClassMetricsExtraT = JSON.parse(this.PerformanceResults[66])
+      const PerClassMetricsExtraT = JSON.parse(this.PerformanceResults[74])
-      const PerClassMetricsAdaB = JSON.parse(this.PerformanceResults[74])
+      const PerClassMetricsAdaB = JSON.parse(this.PerformanceResults[83])
-      const PerClassMetricsGradB = JSON.parse(this.PerformanceResults[82])
+      const PerClassMetricsGradB = JSON.parse(this.PerformanceResults[92])
 
       var KNNModels = []
       var SVCModels = []
@@ -461,8 +462,36 @@ export default {
         }
 
         for (var i = 0; i < target_names.length; i++) {
-          traces[i] = {
+          if (this.tNameAll == target_names[i]) {
-            x:  ['K-Nearest Neighbors','C-Support Vector Classifier','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
+            traces[i] = {
+            x:  ['K-Nearest Neighbors','C-Support Vector Classif','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
+            y: sumList[i],
+            name: '<b>'+target_names[i]+'</b>',
+            opacity: 0.5,
+            marker: {
+                opacity: 0.5,
+                color: this.colorsValues[i]
+            },
+            type: 'bar'
+            };
+          tracesSel[i] = {
+              type: 'bar',
+              x: ['K-Nearest Neighbors','C-Support Vector Classif','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
+              y: sumLineList[i],
+              name: '<b>'+target_names[i]+' (Sel)</b>',
+              xaxis: 'x2',
+              mode: 'markers',
+              marker: {
+                  opacity: 1.0,
+                  color: this.colorsValues[i],
+              },
+              width: [0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06]
+              };
+              data.push(traces[i])
+              data.push(tracesSel[i])
+          } else {
+            traces[i] = {
+            x:  ['K-Nearest Neighbors','C-Support Vector Classif','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
             y: sumList[i],
             name: target_names[i],
             opacity: 0.5,
@@ -474,7 +503,7 @@ export default {
             };
           tracesSel[i] = {
               type: 'bar',
-              x: ['K-Nearest Neighbors','C-Support Vector Classifier','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
+              x: ['K-Nearest Neighbors','C-Support Vector Classif','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
               y: sumLineList[i],
               name: target_names[i]+' (Sel)',
               xaxis: 'x2',
@@ -488,15 +517,19 @@ export default {
               data.push(traces[i])
               data.push(tracesSel[i])
           }
+          
+          }
           var barc = document.getElementById('barChart');
+          var config = {scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage'], responsive: true}
 
-          Plotly.newPlot(barc, data, layout)
+          Plotly.newPlot(barc, data, layout, config)
 
           barc.on('plotly_click', (eventData) => {
             var tName 
             eventData.points.forEach((e) => {
               tName = e.data.name.replace(/ *\([^)]*\) */g, "")
             });
+            this.tNameAll = tName
             EventBus.$emit('clearPCP')
             EventBus.$emit('alternateFlagLock')
             EventBus.$emit('boxplotSet', [storeKNN[tName],storeSVC[tName],storeGausNB[tName],storeMLP[tName],storeLR[tName],storeLDA[tName],storeQDA[tName],storeRF[tName],storeExtraT[tName],storeAdaB[tName],storeGradB[tName]])
@@ -511,6 +544,8 @@ export default {
       }
     },
     mounted() {
+      EventBus.$on('EraseSelectionBarChart', data => { this.tNameAll = data })
+
       EventBus.$on('updateBarChartAlgorithm', data => { this.algorithmsinBar = data })
       EventBus.$on('updateBarChart', data => { this.modelsSelectedinBar = data })
       EventBus.$on('updateBarChart', this.BarChartView)

frontend/src/components/DataSpace.vue

@@ -1,15 +1,17 @@
 <template>
   <div>
     <div align="center">
-      Projection Selection: <select id="selectBarChartData" @change="selectVisualRepresentationData()">
+      Projection Method: <select id="selectBarChartData" @change="selectVisualRepresentationData()">
-        <option value="mds" selected>MDS Projection</option>
+        <option value="mds" selected>MDS</option>
-        <option value="tsne">t-SNE Projection</option>
+        <option value="tsne">t-SNE</option>
-        <option value="umap">UMAP Projection</option>
+        <option value="umap">UMAP</option>
       </select>
+      &nbsp;&nbsp;
       Filter: <select id="selectFilterID" @change="selectAppliedFilter()">
         <option value="mean" selected>Mean</option>
         <option value="median">Median</option>
       </select>
+      &nbsp;&nbsp;
       Action: <button
       id="mergeID"
       v-on:click="merge">
@@ -28,6 +30,7 @@
       <font-awesome-icon icon="eraser" />
       {{ removeData }}
       </button>
+      &nbsp;&nbsp;
       History Manager: <button
       id="saveID"
       v-on:click="save">
@@ -62,7 +65,6 @@ export default {
       composeData: 'Compose',
       saveData: 'Save Step',
       restoreData: 'Restore Step',
-      instanceImpSize: '',
       userSelectedFilter: 'mean',
       responsiveWidthHeight: [],
       colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
@@ -97,6 +99,15 @@ export default {
     restore () {
       EventBus.$emit('SendProvenance', 'restore')
     },
+    clean(obj) {
+      var propNames = Object.getOwnPropertyNames(obj);
+      for (var i = 0; i < propNames.length; i++) {
+        var propName = propNames[i];
+        if (obj[propName] === null || obj[propName] === undefined) {
+          delete obj[propName];
+        }
+      }
+    },
     scatterPlotDataView () {
       Plotly.purge('OverviewDataPlotly')
   
@@ -110,8 +121,30 @@ export default {
       const DataSetY = JSON.parse(this.dataPoints[3])
       const originalDataLabels = JSON.parse(this.dataPoints[4])
       var DataSetParse = JSON.parse(DataSet)
+      var stringParameters = []
+      for (let i = 0; i < DataSetParse.length; i++) {
+        this.clean(DataSetParse[i])
+        stringParameters.push(JSON.stringify(DataSetParse[i]).replace(/,/gi, '<br>'))
+      }
       const XandYCoordinatesTSNE = JSON.parse(this.dataPoints[5])
       const XandYCoordinatesUMAP = JSON.parse(this.dataPoints[6])
+      const impSizeArray = JSON.parse(this.dataPoints[7])
+      const KNNSize = JSON.parse(impSizeArray[8])
+      const SVCSize = JSON.parse(impSizeArray[17])
+      const GausNBSize = JSON.parse(impSizeArray[26])
+      const MLPSize = JSON.parse(impSizeArray[35])
+      const LRSize = JSON.parse(impSizeArray[44])
+      const LDASize = JSON.parse(impSizeArray[53])
+      const QDASize = JSON.parse(impSizeArray[62])
+      const RFSize = JSON.parse(impSizeArray[71])
+      const ExtraTSize = JSON.parse(impSizeArray[80])
+      const AdaBSize = JSON.parse(impSizeArray[89])
+      const GradBSize = JSON.parse(impSizeArray[98])
+      var sizeScatterplot = []
+
+      for (let i = 0; i < KNNSize.length; i++) {
+        sizeScatterplot.push(((KNNSize[i] + SVCSize[i] + GausNBSize[i] + MLPSize[i] + LRSize[i] + LDASize[i] + QDASize[i] + RFSize[i] + ExtraTSize[i] + AdaBSize[i] + GradBSize[i]) / 11) * 12)
+      }
 
       let intData = []
       if (this.highlightedPoints.length > 0){
@@ -126,13 +159,6 @@ export default {
       var Xaxs = []
       var Yaxs = []
       var Opacity
-      var impSizeArray 
-
-      if (this.instanceImpSize.length != 0) {
-        impSizeArray = JSON.parse(this.instanceImpSize)
-      }
-      
-      console.log(impSizeArray)
     
       if (this.representationDef == 'mds') {
         for (let i = 0; i < XandYCoordinatesMDS[0].length; i++) {
@@ -154,7 +180,7 @@ export default {
           const aux_ID = result.ID.filter((item, index) => originalDataLabels[index] == target_names[i]);
 
           var Text = aux_ID.map((item, index) => {
-            let popup = 'Data Point ID: ' + item + '; Details: ' + JSON.stringify(DataSetParse[item])
+            let popup = 'Data Point ID: ' + item + '<br> Details: ' + stringParameters[item]
             return popup;
           });
 
@@ -176,7 +202,7 @@ export default {
               y: aux_Y,
               mode: 'markers',
               name: target_names[i],
-              marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: impSizeArray },
+              marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: sizeScatterplot },
               hovertemplate: 
                       "<b>%{text}</b><br><br>" +
                       "<extra></extra>",
@@ -185,7 +211,7 @@ export default {
         }
 
         layout = {
-        title: 'Data Space Projection (MDS)',
+        title: 'MDS Projection',
         xaxis: {
             visible: false
         },
@@ -197,6 +223,13 @@ export default {
         autosize: true,
         width: width,
         height: height,
+        margin: {
+            l: 50,
+            r: 0,
+            b: 30,
+            t: 40,
+            pad: 0
+          },
         }
       } else if (this.representationDef == 'tsne') {
         result = XandYCoordinatesTSNE.reduce(function(r, a) {
@@ -227,7 +260,7 @@ export default {
           const aux_ID = result.ID.filter((item, index) => originalDataLabels[index] == target_names[i]);
 
           var Text = aux_ID.map((item, index) => {
-            let popup = 'Data Point ID: ' + item + '; Details: ' + JSON.stringify(DataSetParse[item])
+            let popup = 'Data Point ID: ' + item + '<br> Details: ' + stringParameters[item]
             return popup;
           });
 
@@ -248,7 +281,7 @@ export default {
             y: aux_Y,
             mode: 'markers',
             name: target_names[i],
-            marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: impSizeArray },
+            marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: sizeScatterplot },
             hovertemplate: 
                     "<b>%{text}</b><br><br>" +
                     "<extra></extra>",
@@ -257,7 +290,7 @@ export default {
         }
 
         layout = {
-        title: 'Data Space Projection (t-SNE)',
+        title: 't-SNE Projection',
         xaxis: {
             visible: false
         },
@@ -269,6 +302,13 @@ export default {
         autosize: true,
         width: width,
         height: height,
+        margin: {
+            l: 50,
+            r: 0,
+            b: 30,
+            t: 40,
+            pad: 0
+          },
         }
       } else {
         for (let i = 0; i < XandYCoordinatesUMAP[0].length; i++) {
@@ -289,7 +329,7 @@ export default {
           const aux_ID = result.ID.filter((item, index) => originalDataLabels[index] == target_names[i]);
 
           var Text = aux_ID.map((item, index) => {
-            let popup = 'Data Point ID: ' + item + '; Details: ' + JSON.stringify(DataSetParse[item])
+            let popup = 'Data Point ID: ' + item + '<br> Details: ' + stringParameters[item]
             return popup;
           });
 
@@ -310,7 +350,7 @@ export default {
               y: aux_Y,
               mode: 'markers',
               name: target_names[i],
-              marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: impSizeArray },
+              marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: sizeScatterplot },
               hovertemplate: 
                       "<b>%{text}</b><br><br>" +
                       "<extra></extra>",
@@ -319,7 +359,7 @@ export default {
         }
 
         layout = {
-        title: 'Data Space Projection (UMAP)',
+        title: 'UMAP Projection',
         xaxis: {
             visible: false
         },
@@ -331,6 +371,13 @@ export default {
         autosize: true,
         width: width,
         height: height,
+        margin: {
+            l: 50,
+            r: 0,
+            b: 30,
+            t: 40,
+            pad: 0
+          },
         }
       }
 
@@ -368,8 +415,6 @@ export default {
     }
   },
   mounted() {
-    EventBus.$on('emittedEventCallingDataSpaceImportance', data => { this.instanceImpSize = data })
-
     // initialize the first data space projection based on the data set 
     EventBus.$on('emittedEventCallingDataSpacePlotView', data => {
       this.dataPoints = data})

frontend/src/components/Export.vue

@@ -1,18 +1,58 @@
 <template>
-  <div id="ExportResults">Results go here</div>
+  <div id="ExportResults">
+  Data Instances: {{ DataPickled }}
+  <br>
+  =======================================================
+  <br>
+  Data Features Per Model: {{ FeaturesPickled }}
+  <br>
+  =======================================================
+  <br>
+  Models IDs and Parameters: {{ ModelsPickled }}
+  </div>
 </template>
 
 <script>
 
 import { EventBus } from '../main.js'
-
+import * as Cryo from 'cryo'
 export default {
   name: 'Export',
   data () {
     return {
+      DataPickled: '',
+      FeaturesPickled: '',
+      ModelsPickled: '',
+      stackData: [],
+      stackFeatures: [],
+      stackModels: [],
     }
   },
   methods: {
+    Pickle () {
+      this.DataPickled = Cryo.stringify(this.stackData)
+      this.FeaturesPickled = Cryo.stringify(this.stackFeatures)
+      this.ModelsPickled = Cryo.stringify(this.stackModels)
+    }
+  },
+  mounted () {
+    EventBus.$on('sendDatatoPickle', data => {
+    this.stackData = data})
+    EventBus.$on('sendDatatoPickle', this.Pickle)
+
+    EventBus.$on('sendSelectedFeaturestoPickle', data => {
+    this.stackFeatures = data})
+    EventBus.$on('sendSelectedFeaturestoPickle', this.Pickle)
+
+    EventBus.$on('ExtractResults', data => {
+    this.stackModels = data})
+    EventBus.$on('ExtractResults', this.Pickle)
   }
 }
 </script>
+
+<style scoped>
+#ExportResults {
+  word-break: break-all !important;
+}
+</style>

frontend/src/components/Heatmap.vue

@@ -123,7 +123,7 @@ export default {
               else if (this.Toggles[0] == 0 && this.Toggles[1] == 0 && this.Toggles[2] == 1) {
                 values[j] = FeaturesAccuracy[j][i]*100
               } else {
-                alert('Please, keep at least one toggle active! The states of the toggles are being reset.') // Fix this!
+                alert('Please, keep at least one toggle active! The states of the toggles are being reset.')
                 this.Toggles[0] = 1
                 this.Toggles[1] = 1
                 this.Toggles[2] = 1
@@ -382,6 +382,7 @@ export default {
                 }
                 finalresults.push(results)
             }
+            EventBus.$emit('sendSelectedFeaturestoPickle', finalresults)
             EventBus.$emit('SendSelectedFeaturesEvent', finalresults)
           });
       var svgLeg = d3.select("#LegendHeat").append("svg")

frontend/src/components/Main.vue

@@ -143,7 +143,7 @@
     <div id="myModal" class="w3-modal" style="position: fixed;">
       <div class="w3-modal-content w3-card-4 w3-animate-zoom">
         <header class="w3-container w3-blue"> 
-        <h3 style="display:inline-block; font-size: 16px; margin-top: 15px; margin-bottom:15px">Serialized Ensemble Learning Models Using Pickling</h3>
+        <h3 style="display:inline-block; font-size: 16px; margin-top: 15px; margin-bottom:15px">Serialized Data and Stacking Ensemble Learning Models using Cryo</h3>
         </header>
         <Export/>
         <div class="w3-container w3-light-grey w3-padding">
@@ -323,6 +323,7 @@ export default Vue.extend({
           if (this.firstTimeFlag == 1) {
             this.selectedModels_Stack.push(0)
             this.selectedModels_Stack.push(JSON.stringify(this.modelsUpdate))
+            EventBus.$emit('ParametersProvenance', this.OverviewResults)
             EventBus.$emit('InitializeProvenance', this.selectedModels_Stack)
           }
           this.firstTimeFlag = 0
@@ -555,8 +556,6 @@ export default Vue.extend({
       axios.get(path, axiosConfig)
         .then(response => {
           this.FinalResults = response.data.FinalResults
-
-          this.DataSpaceImportance()
           EventBus.$emit('emittedEventCallingLinePlot', this.FinalResults)
         })
         .catch(error => {
@@ -611,27 +610,6 @@ export default Vue.extend({
           console.log(error)
         })
     },
-    DataSpaceImportance () {
-       const path = `http://localhost:5000/data/SendInstancesImportance`
-
-      const axiosConfig = {
-        headers: {
-          'Content-Type': 'application/json',
-          'Access-Control-Allow-Origin': '*',
-          'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
-          'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
-        }
-      }
-      axios.get(path, axiosConfig)
-        .then(response => {
-          this.instancesImportance = response.data.instancesImportance
-          EventBus.$emit('emittedEventCallingDataSpaceImportance', this.instancesImportance)
-          this.DataSpaceCall()
-        })
-        .catch(error => {
-          console.log(error)
-        })
-    },
     DataSpaceCallAfterDataManipulation () {
       const path = `http://localhost:5000/data/requestDataSpaceResultsAfterDataManipulation`
 
@@ -645,7 +623,8 @@ export default Vue.extend({
       }
       axios.get(path, axiosConfig)
         .then(response => {
-          this.DataSpaceImportance()
+          console.log('Calling Data Space!')
+          this.DataSpaceCall()
         })
         .catch(error => {
           console.log(error)
@@ -932,6 +911,23 @@ export default Vue.extend({
           console.log(error)
         })
     },
+    Alg () {
+      $('#profile-tab').on('click', function (e) {
+        EventBus.$emit('Algorithm', false)
+      })
+
+      $('#contact-tab').on('click', function (e) {
+        EventBus.$emit('Algorithm', false)
+      })
+
+      $('#home-tab').on('click', function (e) {
+        var delayInMilliseconds = 1000; //1 second
+
+        setTimeout(function() {
+          EventBus.$emit('Algorithm', true)
+        }, delayInMilliseconds);
+      })
+    },
   },
   created () {
     // does the browser support the Navigation Timing API?
@@ -946,6 +942,7 @@ export default Vue.extend({
     window.addEventListener('resize', this.change)
   },
   mounted() {
+    this.Alg()
     var modal = document.getElementById('myModal')
     window.onclick = function(event) {
       //alert(event.target)

frontend/src/components/PCPData.vue

@@ -1,5 +1,5 @@
 <template>
-  <div id="PCPDataView" class="parcoords" style="width: 1200px; height:280px"></div>
+  <div id="PCPDataView" class="parcoords"></div>
 </template>
 
 <script>
@@ -17,7 +17,7 @@ export default {
   data () {
     return {
       PCPDataReceived: '',
-      colorsValues: ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99']
+      colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5']
     }
   },
   methods: {
@@ -29,10 +29,19 @@ export default {
       const DataSetNew = JSON.parse(this.PCPDataReceived[2])
       var DataSetParse = JSON.parse(DataSetNew)
       const target_names = JSON.parse(this.PCPDataReceived[3])
-      var colors = this.colorsValues
+      const target_names_original = JSON.parse(this.PCPDataReceived[4])
+
+      var extraction = []
+      for (let i = 0; i < DataSetParse.length; i++) {
+        extraction.push(Object.assign(DataSetParse[i], {Outcome: target_names_original[i]}))
+      }
       
-      this.pc = ParCoords()("#PCPDataView")
+      var colors = this.colorsValues
+      EventBus.$emit('sendDatatoPickle', extraction)
+      var pc = ParCoords()("#PCPDataView")
           .data(DataSetParse)
+          .width(1200)
+          .height(280)
           .color(function(d, i) { return colors[target_names[i]] })
           .bundlingStrength(0) // set bundling strength
           .smoothness(0)

frontend/src/components/Parameters.vue

@@ -73,7 +73,7 @@ export default {
       labelFactor: 1.25, 			//How much farther than the radius of the outer circle should the labels be placed
       wrapWidth: 60, 			//The number of pixels after which a label needs to be given a new line
       opacityArea: 0.35, 			//The opacity of the area of the blob
-      dotRadius: 4, 				//The size of the colored circles of each blog
+      dotRadius: 2, 				//The size of the colored circles of each blog
       opacityCircles: 0.1, 			//The opacity of the circles of each blob
       strokeWidth: 2, 			//The width of the stroke around each blob
       roundStrokes: false,			//If true the area and stroke will follow a round path (cardinal-closed)
@@ -387,7 +387,6 @@ export default {
       // Clear Heatmap first
       var svg = d3.select("#overview");
       svg.selectAll("*").remove();
-
       var widthinter = this.WH[0]*2 // interactive visualization
       var heightinter = this.WH[1]*1.23 // interactive visualization
 
@@ -496,7 +495,7 @@ export default {
         } else if (this.storeActiveModels[0] > this.AdaBModels) {
           this.allActiveAdaB = countAdaBRelated.slice()
         } else if (this.storeActiveModels[0] > this.ExtraTModels) {
-          this.allActiveExtraT = countExtraT.slice()
+          this.allActiveExtraT = countExtraTRelated.slice()
         } else if (this.storeActiveModels[0] > this.RFModels) {
           this.allActiveRF = countRFRelated.slice()
         } else if (this.storeActiveModels[0] > this.QDAModels) {

frontend/src/components/PerMetricBarChart.vue

@@ -12,53 +12,86 @@ export default {
     return {
       barchartmetrics: '',
       WH: [],
-      SelBarChartMetrics: []
+      SelBarChartMetrics: [],
+      factors: [1,1,1,0,0
+      ,1,0,0,1,0
+      ,0,1,0,0,0
+      ,0,0,1,0,0
+      ,0,1,1,1
+      ],
     }
   },
   methods: {
     LineBar () {
       Plotly.purge('PerMetricBar')
 
+      var x = []
       var metricsPerModel = JSON.parse(this.barchartmetrics[9])
+
       var metricsPerModelSel = []
       if (this.SelBarChartMetrics.length == 0) {
         metricsPerModelSel = metricsPerModel
       } else {
         metricsPerModelSel = this.SelBarChartMetrics
       }
-      console.log(metricsPerModel)
+
-      console.log(metricsPerModelSel)
+      var factorsLocal = this.factors
+
+      var perModelAllClear = []
+      var perModelSelectedClear = []
+      var resultsColors = []
+      var chooseFrom = ['Accuracy','MAE','RMSE','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','MCC','ROC AUC','Log Loss']
+      for (let i = 0; i < metricsPerModel.length; i++) {
+        if (factorsLocal[i] != 0) {
+          resultsColors.push(metricsPerModel[i])
+        }
+        var temp = metricsPerModel[i]
+        var resultsClear = JSON.parse(temp)
+        var tempSel = metricsPerModelSel[i]
+        var resultsClearSelected = JSON.parse(tempSel)
+        for (let j = 0; j < Object.values(resultsClear).length; j++) {
+          if (factorsLocal[i] != 0) {
+            perModelAllClear.push(Object.values(resultsClear)[j])
+            perModelSelectedClear.push(Object.values(resultsClearSelected)[j])
+            x.push(chooseFrom[i])
+          }
+        }
+      }
       var width = this.WH[0]*6.5 // interactive visualization
       var height = this.WH[1]*0.5 // interactive visualization
           var trace1 = {
-          x: ['Accuracy','MAE','RMSE','G-Mean','Precision','Recall','F-Beta Sc','MCC','ROC AUC','Log Loss'], 
+          x: x,
-          y: metricsPerModel, 
+          y: perModelAllClear, 
-          name: 'Projection average', 
+          name: 'Performance Metrics', 
-          type: 'bar',
+          type: 'box',
+          boxmean: true,
           marker: {
               color: 'rgb(0,0,0)'
           }
           };
           var trace2 = {
-          x: ['Accuracy','MAE','RMSE','G-Mean','Precision','Recall','F-Beta Sc','MCC','ROC AUC','Log Loss'],
+          x: x,
-          y: metricsPerModelSel, 
+          y: perModelSelectedClear, 
           name: 'Selected points', 
-          type: 'bar',
+          type: 'box',
+          boxmean: true,
           marker: {
               color: 'rgb(211,211,211)'
           }
           };
           var data = [trace1, trace2];
           var layout = {
-          barmode: 'group',autosize: false,
+          boxmode: 'group',
+          autosize: true,
           width:  width,
           height: height,
+          hovermode: 'x',
           margin: {
-              l: 50,
+            l: 50,
-              r: 30,
+            r: 0,
-              b: 35,
+            b: 30,
-              t: 5,
+            t: 40,
-              pad: 4
+            pad: 0
           },
           xaxis: {
               title: 'Performance Metrics',
@@ -67,13 +100,61 @@ export default {
               color: 'black'
               }},
           yaxis: {
-              title: 'Performance',
+              title: '# Performance (%) #',
               titlefont: {
               size: 12,
               color: 'black'
               }}};
+          var boxPlot = document.getElementById('PerMetricBar');
+          var config = {scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage'], responsive: true}
+          Plotly.newPlot(boxPlot, data, layout, config);
 
-        Plotly.newPlot('PerMetricBar', data, layout, {displayModeBar:false}, {staticPlot: true});
+          boxPlot.on('plotly_click', (eventData) => {
+            var xAxisHovered
+            xAxisHovered = eventData.points[0].x
+            var index
+            if (xAxisHovered == 'Accuracy') {
+              Plotly.restyle(boxPlot, 'x', [['<b>Accuracy</b>','MAE','RMSE','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','MCC','ROC AUC','Log Loss']]);
+              index = 0
+            }
+            else if (xAxisHovered == 'MAE') {
+              Plotly.restyle(boxPlot, 'x', [['Accuracy','<b>MAE</b>','RMSE','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','MCC','ROC AUC','Log Loss']]);
+              index = 1
+            }
+            else if (xAxisHovered == 'RMSE') {
+              Plotly.restyle(boxPlot, 'x', [['Accuracy','MAE','<b>RMSE</b>','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','MCC','ROC AUC','Log Loss']]);
+              index = 2
+            }
+            else if (xAxisHovered == 'G-Mean') {
+              Plotly.restyle(boxPlot, 'x', [['Accuracy','MAE','RMSE','<b>G-Mean</b>','<b>G-Mean</b>','<b>G-Mean</b>','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','MCC','ROC AUC','Log Loss']]);
+              index = 3
+            }
+            else if (xAxisHovered == 'Precision') {
+              Plotly.restyle(boxPlot, 'x', [['Accuracy','MAE','RMSE','G-Mean','G-Mean','G-Mean','<b>Precision</b>','<b>Precision</b>','<b>Precision</b>','Recall','Recall','Recall','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','MCC','ROC AUC','Log Loss']]);
+              index = 4
+            }
+            else if (xAxisHovered == 'Recall') {
+              Plotly.restyle(boxPlot, 'x', [['Accuracy','MAE','RMSE','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','<b>Recall</b>','<b>Recall</b>','<b>Recall</b>','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','MCC','ROC AUC','Log Loss']]);
+              index = 5
+            }
+            else if (xAxisHovered == 'F-Beta Sc') {
+              Plotly.restyle(boxPlot, 'x', [['Accuracy','MAE','RMSE','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','<b>F-Beta Sc</b>','<b>F-Beta Sc</b>','<b>F-Beta Sc</b>','<b>F-Beta Sc</b>','<b>F-Beta Sc</b>','<b>F-Beta Sc</b>','<b>F-Beta Sc</b>','<b>F-Beta Sc</b>','<b>F-Beta Sc</b>','MCC','ROC AUC','Log Loss']]);
+              index = 6
+            }
+            else if (xAxisHovered == 'MCC') {
+              Plotly.restyle(boxPlot, 'x', [['Accuracy','MAE','RMSE','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','<b>MCC</b>','ROC AUC','Log Loss']]);
+              index = 7
+            }
+            else if (xAxisHovered == 'ROC AUC') {
+              Plotly.restyle(boxPlot, 'x', [['Accuracy','MAE','RMSE','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','MCC','<b>ROC AUC</b>','Log Loss']]);
+              index = 8
+            }
+            else {
+              Plotly.restyle(boxPlot, 'x', [['Accuracy','MAE','RMSE','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','MCC','ROC AUC','<b>Log Loss</b>']]);
+              index = 9
+            }
+            EventBus.$emit('updateMetricsScatter', resultsColors[index])
+          });
       },
       reset () {
         Plotly.purge('PerMetricBar')
@@ -82,14 +163,22 @@ export default {
     mounted () {
       EventBus.$on('InitializeMetricsBarChart', data => {this.barchartmetrics = data;})
       EventBus.$on('InitializeMetricsBarChart', this.LineBar)
+
       EventBus.$on('Responsive', data => {
       this.WH = data})
       EventBus.$on('ResponsiveandChange', data => {
       this.WH = data})
+
       EventBus.$on('UpdateBarChartperMetric', data => {
       this.SelBarChartMetrics = data})
       EventBus.$on('UpdateBarChartperMetric', this.LineBar)
 
+      EventBus.$on('CallFactorsView', data => {
+      this.factors = data})
+      EventBus.$on('CallFactorsView', this.LineBar)
+
+      EventBus.$on('updateBoxPlots', this.LineBar)
+
       // reset view
       EventBus.$on('resetViews', this.reset)
     }

frontend/src/components/PredictionsSpace.vue

@@ -2,10 +2,10 @@
 <div>
   <b-row class="md-3">
     <b-col cols="12">
-      <div>Projection Selection: <select id="selectBarChartPred" @change="selectVisualRepresentationPred()">
+      <div>Projection Method: <select id="selectBarChartPred" @change="selectVisualRepresentationPred()">
-        <option value="mds" selected>MDS Projection</option>
+        <option value="mds" selected>MDS</option>
-        <option value="tsne">t-SNE Projection</option>
+        <option value="tsne">t-SNE</option>
-        <option value="umap">UMAP Projection</option>
+        <option value="umap">UMAP</option>
       </select>
       <div id="OverviewPredPlotly" class="OverviewPredPlotly"></div>
       </div>
@@ -39,6 +39,15 @@ export default {
     reset () {
       Plotly.purge('OverviewPredPlotly')
     },
+    clean(obj) {
+      var propNames = Object.getOwnPropertyNames(obj);
+      for (var i = 0; i < propNames.length; i++) {
+        var propName = propNames[i];
+        if (obj[propName] === null || obj[propName] === undefined) {
+          delete obj[propName];
+        }
+      }
+    },
     ScatterPlotPredView () {
        Plotly.purge('OverviewPredPlotly')
 
@@ -52,6 +61,11 @@ export default {
       const DataSetY = JSON.parse(this.PredictionsData[15])
       const originalDataLabels = JSON.parse(this.PredictionsData[16])
       var DataSetParse = JSON.parse(DataSet)
+      var stringParameters = []
+      for (let i = 0; i < DataSetParse.length; i++) {
+        this.clean(DataSetParse[i])
+        stringParameters.push(JSON.stringify(DataSetParse[i]).replace(/,/gi, '<br>'))
+      }
       const XandYCoordinatesTSNE = JSON.parse(this.PredictionsData[18])
       const XandYCoordinatesUMAP= JSON.parse(this.PredictionsData[19])
 
@@ -80,7 +94,7 @@ export default {
           const aux_ID = result.ID.filter((item, index) => originalDataLabels[index] == target_names[i]);
 
           var Text = aux_ID.map((item, index) => {
-            let popup = 'Data Point ID: ' + item + '; Details: ' + JSON.stringify(DataSetParse[item])
+            let popup = 'Data Point ID: ' + item + '<br> Details: ' + stringParameters[item]
             return popup;
           });
 
@@ -98,7 +112,7 @@ export default {
         }
 
         layout = {
-        title: 'Predictions Space Projection (MDS)',
+        title: 'MDS Projection',
         xaxis: {
             visible: false
         },
@@ -110,6 +124,13 @@ export default {
         autosize: true,
         width: width,
         height: height,
+        margin: {
+            l: 50,
+            r: 0,
+            b: 30,
+            t: 40,
+            pad: 0
+          },
         }
       } else if (this.representationDef == 'tsne') {
         result = XandYCoordinatesTSNE.reduce(function(r, a) {
@@ -140,7 +161,7 @@ export default {
           const aux_ID = result.ID.filter((item, index) => originalDataLabels[index] == target_names[i]);
 
           var Text = aux_ID.map((item, index) => {
-            let popup = 'Data Point ID: ' + item + '; Details: ' + JSON.stringify(DataSetParse[item])
+            let popup = 'Data Point ID: ' + item + '<br> Details: ' + stringParameters[item]
             return popup;
           });
 
@@ -158,7 +179,7 @@ export default {
         }
 
         layout = {
-        title: 'Prediction Space Projection (t-SNE)',
+        title: 't-SNE Projection',
         xaxis: {
             visible: false
         },
@@ -170,6 +191,13 @@ export default {
         autosize: true,
         width: width,
         height: height,
+        margin: {
+            l: 50,
+            r: 0,
+            b: 30,
+            t: 40,
+            pad: 0
+          },
         }
       } else {
         for (let i = 0; i < XandYCoordinatesUMAP[0].length; i++) {
@@ -190,7 +218,7 @@ export default {
           const aux_ID = result.ID.filter((item, index) => originalDataLabels[index] == target_names[i]);
 
           var Text = aux_ID.map((item, index) => {
-            let popup = 'Data Point ID: ' + item + '; Details: ' + JSON.stringify(DataSetParse[item])
+            let popup = 'Data Point ID: ' + item + '<br> Details: ' + stringParameters[item]
             return popup;
           });
 
@@ -208,7 +236,7 @@ export default {
         }
 
         layout = {
-        title: 'Predictions Space Projection (UMAP)',
+        title: 'UMAP Projection',
         xaxis: {
             visible: false
         },
@@ -220,6 +248,13 @@ export default {
         autosize: true,
         width: width,
         height: height,
+        margin: {
+            l: 50,
+            r: 0,
+            b: 30,
+            t: 40,
+            pad: 0
+          },
         }
       }
 

frontend/src/components/Provenance.vue

@@ -37,6 +37,7 @@ export default {
       ExtraTModels: 2606,
       AdaBModels: 2766,
       GradBModels: 2926,
+      AllDetails: '',
       platform: ''
     }
   },
@@ -48,6 +49,15 @@ export default {
         this.platform.clear();
       }
     },
+    clean(obj) {
+      var propNames = Object.getOwnPropertyNames(obj);
+      for (var i = 0; i < propNames.length; i++) {
+        var propName = propNames[i];
+        if (obj[propName] === null || obj[propName] === undefined) {
+          delete obj[propName];
+        }
+      }
+    },
     provenance () {
       var canvas = document.getElementById("main-canvas");
       var width = this.WH[0]*9 // interactive visualization
@@ -66,6 +76,18 @@ export default {
       var flagGradB = 0
 
       var StackInfo = JSON.parse(this.stackInformation[1])
+
+      var parameters = JSON.parse(this.AllDetails[2])
+      var parameters = JSON.parse(parameters)
+
+      var stringParameters = []
+      var temp = 0
+      for (let i = 0; i < StackInfo.length; i++) {
+        this.clean(parameters[i])
+        temp = JSON.stringify(Object.assign({ID: StackInfo[i]}, parameters[i]))
+        stringParameters.push(temp)
+      }
+
       // Create a WebGL 2D platform on the canvas:
       this.platform = Stardust.platform("webgl-2d", canvas, width, height);
     
@@ -211,11 +233,14 @@ export default {
 
       isotypes.data(this.data);
 
+      EventBus.$emit('ExtractResults', stringParameters)
+
       isotypes.render();
       this.counter = this.counter + 1
   }
   },
   mounted () {
+    EventBus.$on('ParametersProvenance', data => {this.AllDetails = data})
     EventBus.$on('InitializeProvenance', data => {this.stackInformation = data})
     EventBus.$on('InitializeProvenance', this.provenance)
     EventBus.$on('Responsive', data => {

frontend/src/components/ResetClass.vue

@@ -21,6 +21,7 @@ export default {
   methods: {
       resetClass () {
         EventBus.$emit('clearPCP')
+        EventBus.$emit('EraseSelectionBarChart', '')
         EventBus.$emit('alternateFlagLock')
         EventBus.$emit('boxplotCall', true)
       }

frontend/src/components/ScatterPlot.vue

@@ -1,17 +1,25 @@
 <template>
 <div>
   <div align="center">
-            Projection Selection: <select id="selectBarChart" @change="selectVisualRepresentation()">
+            Projection Method: <select id="selectBarChart" @change="selectVisualRepresentation()">
-              <option value="mds" selected>MDS Projection</option>
+              <option value="mds" selected>MDS</option>
-              <option value="tsne">t-SNE Projection</option>
+              <option value="tsne">t-SNE</option>
-              <option value="umap">UMAP Projection</option>
+              <option value="umap">UMAP</option>
             </select>
+            &nbsp;&nbsp;
             Action: <button
             id="RemoveStack"
             v-on:click="RemoveStack">
             <font-awesome-icon icon="minus" />
             {{ valueStackRemove }}
             </button>
+            &nbsp;&nbsp;
+            Filter: <button
+            id="ResetSelection"
+            v-on:click="resetSelection">
+            <font-awesome-icon icon="sync-alt" />
+            {{ valueResetSel }}
+            </button>
   </div>
   <div id="OverviewPlotly" class="OverviewPlotly"></div>
 </div>
@@ -38,14 +46,21 @@ export default {
       max: 0,
       min: 0,
       WH: [],
+      newColorsUpdate: [],
       parametersAll: [],
       length: 0,
-      valueStackRemove: 'Remove from Stack',
+      valueStackRemove: 'Remove Unselected from Stack',
       DataPointsSelUpdate: [],
-      ModelsIDGray: []
+      ModelsIDGray: [],
+      valueResetSel: 'Reset Metric Selection'
     }
   },
   methods: {
+    resetSelection () {
+      this.newColorsUpdate = []
+      this.ScatterPlotView()
+      EventBus.$emit('updateBoxPlots')
+    },
     reset () {
       Plotly.purge('OverviewPlotly')
     },
@@ -57,10 +72,27 @@ export default {
     RemoveStack () {
       EventBus.$emit('RemoveFromStack')
     },
+    clean(obj) {
+      var propNames = Object.getOwnPropertyNames(obj);
+      for (var i = 0; i < propNames.length; i++) {
+        var propName = propNames[i];
+        if (obj[propName] === null || obj[propName] === undefined) {
+          delete obj[propName];
+        }
+      }
+    },
     ScatterPlotView () {
       Plotly.purge('OverviewPlotly')
+
       var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0])
 
+      if (this.newColorsUpdate.length != 0) {
+        let resultsClear = JSON.parse(this.newColorsUpdate)
+        for (let j = 0; j < Object.values(resultsClear).length; j++) {
+          colorsforScatterPlot.push(Object.values(resultsClear)[j])
+        }
+      }
+
       var MDSData = JSON.parse(this.ScatterPlotResults[1])
       var parameters = JSON.parse(this.ScatterPlotResults[2])
       var TSNEData = JSON.parse(this.ScatterPlotResults[12])
@@ -69,7 +101,12 @@ export default {
 
       EventBus.$emit('sendPointsNumber', modelId.length)
 
-      parameters = JSON.parse(parameters)
+      var parameters = JSON.parse(parameters)
+      var stringParameters = []
+      for (let i = 0; i < parameters.length; i++) {
+        this.clean(parameters[i])
+        stringParameters.push(JSON.stringify(parameters[i]).replace(/,/gi, '<br>'))
+      }
 
       if (this.colorsforOver.length != 0) {
         if (this.colorsforOver[1].length != 0) {
@@ -83,14 +120,14 @@ export default {
 
       var classifiersInfoProcessing = []
       for (let i = 0; i < modelId.length; i++) {
-        classifiersInfoProcessing[i] = 'Model ID: ' + modelId[i] + '; Details: ' + JSON.stringify(parameters[i])
+        classifiersInfoProcessing[i] = 'Model ID: ' + modelId[i] + '<br> Details: ' + stringParameters[i]
       }
 
       var listofNumbersModelsIDs = []
       var StackModelsIDs = []
       if (this.ModelsIDGray.length != 0) {
         for (let j = 0; j < this.ModelsIDGray.length; j++){
-          listofNumbersModelsIDs.push(parseInt(this.ModelsIDGray[j].replace(/\D/g, "")))
+          listofNumbersModelsIDs.push(parseInt(this.ModelsIDGray[j]))
         }
 
         var parametersNew = []
@@ -110,12 +147,12 @@ export default {
         EventBus.$emit('sendPointsNumber', StackModelsIDs.length)
         var classifiersInfoProcessing = []
         for (let i = 0; i < StackModelsIDs.length; i++) {
-          classifiersInfoProcessing[i] = 'Model ID: ' + StackModelsIDs[i] + '; Details: ' + JSON.stringify(parametersNew[i])
+          classifiersInfoProcessing[i] = 'Model ID: ' + StackModelsIDs[i] + '; Details: ' + stringParameters[i]
         }
         MDSData[0] = MDSDataNewX
         MDSData[1] = MDSDataNewY
         colorsforScatterPlot = colorsforScatterPlotNew
-        EventBus.$emit('NewHeatmapAccordingtoNewStack', StackModelsIDs)
+        //EventBus.$emit('NewHeatmapAccordingtoNewStack', StackModelsIDs)
       }
       var DataGeneral
 
@@ -124,6 +161,9 @@ export default {
       var maxY
       var minY
 
+      var width = this.WH[0]*6.5 // interactive visualization
+      var height = this.WH[1]*1.22 // interactive visualization
+
       var layout
       if (this.representationDef == 'mds') {
         maxX = Math.max(MDSData[0])
@@ -146,16 +186,14 @@ export default {
             size: 12,
             colorscale: 'Viridis',
             colorbar: {
-              title: 'Metrics Average',
+              title: '# Performance (%) #',
               titleside: 'Top'
             },
           }
         
         }]
-        var width = this.WH[0]*6.5 // interactive visualization
-        var height = this.WH[1]*1.22 // interactive visualization
         layout = {
-          title: 'Models Performance (MDS)',
+          title: 'MDS Projection',
           xaxis: {
               visible: false,
               range: [minX, maxX]
@@ -171,6 +209,13 @@ export default {
           hovermode: "closest",
           hoverlabel: { bgcolor: "#FFF" },
           legend: {orientation: 'h', y: -0.3},
+          margin: {
+            l: 50,
+            r: 0,
+            b: 30,
+            t: 40,
+            pad: 0
+          },
         }
       } else if (this.representationDef == 'tsne') {
         var result = TSNEData.reduce(function(r, a) {
@@ -209,7 +254,7 @@ export default {
           }
         }]
         layout = {
-          title: 'Models Performance (t-SNE)',
+          title: 't-SNE Projection',
           xaxis: {
               visible: false,
               range: [minX, maxX]
@@ -225,6 +270,13 @@ export default {
           hovermode: "closest",
           hoverlabel: { bgcolor: "#FFF" },
           legend: {orientation: 'h', y: -0.3},
+          margin: {
+            l: 50,
+            r: 0,
+            b: 30,
+            t: 40,
+            pad: 0
+          },
         }
 
       } else {
@@ -254,10 +306,8 @@ export default {
           }
         
         }]
-        var width = this.WH[0]*6.5 // interactive visualization
-        var height = this.WH[1]*1 // interactive visualization
         layout = {
-          title: 'Models Performance (UMAP)',
+          title: 'UMAP Projection',
           xaxis: {
               visible: false,
               range: [minX, maxX]
@@ -273,6 +323,13 @@ export default {
           hovermode: "closest",
           hoverlabel: { bgcolor: "#FFF" },
           legend: {orientation: 'h', y: -0.3},
+          margin: {
+            l: 50,
+            r: 0,
+            b: 30,
+            t: 40,
+            pad: 0
+          },
         }
       }
      
@@ -286,7 +343,9 @@ export default {
     },
     selectedPointsOverview () {
       const OverviewPlotly = document.getElementById('OverviewPlotly')
+      var allModels = JSON.parse(this.ScatterPlotResults[13])
       OverviewPlotly.on('plotly_selected', function (evt) {
+        var pushModelsRemaining = []
         if (typeof evt !== 'undefined') {
           const ClassifierIDsList = []
           const ClassifierIDsListCleared = []
@@ -302,9 +361,14 @@ export default {
               ClassifierIDsListCleared.push(numberNumb)
             }
           }
-          if (ClassifierIDsList != '') {
+          for (let i = 0; i < allModels.length; i++) {
-            EventBus.$emit('SendSelectedPointsToServerEvent', ClassifierIDsList)
+            if (!ClassifierIDsListCleared.includes(allModels[i])) {
-            EventBus.$emit('SendSelectedPointsToBrushHeatmap', ClassifierIDsListCleared)
+              pushModelsRemaining.push(allModels[i])
+            }
+          }
+          if (allModels != '') {
+            EventBus.$emit('SendSelectedPointsToServerEvent', pushModelsRemaining)
+            EventBus.$emit('SendSelectedPointsToBrushHeatmap', pushModelsRemaining)
           } else {
             EventBus.$emit('SendSelectedPointsToServerEvent', '')
           }
@@ -357,8 +421,12 @@ export default {
     }
   },
   mounted() {
+    EventBus.$on('updateMetricsScatter', data => { this.newColorsUpdate = data })
+    EventBus.$on('updateMetricsScatter', this.ScatterPlotView)
+
     EventBus.$on('GrayOutPoints', data => { this.ModelsIDGray = data })
     EventBus.$on('GrayOutPoints', this.ScatterPlotView)
+
     EventBus.$on('emittedEventCallingBrushedBoxPlot', data => {
       this.brushedBox = data})
     EventBus.$on('emittedEventCallingScatterPlot', data => {

insertMongo.py

@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+import sys
+import pandas as pd
+import pymongo
+import json
+import os
+
+
+def import_content(filepath):
+    mng_client = pymongo.MongoClient('localhost', 27017)
+    mng_db = mng_client['mydb']
+    collection_name = 'DiabetesC' 
+    db_cm = mng_db[collection_name]
+    cdir = os.path.dirname(__file__)
+    file_res = os.path.join(cdir, filepath)
+
+    data = pd.read_csv(file_res)
+    data_json = json.loads(data.to_json(orient='records'))
+    db_cm.remove()
+    db_cm.insert(data_json)
+
+if __name__ == "__main__":
+  filepath = '/Users/anchaa/Documents/Research/StackVis_code/StackVis/diabetes.csv'
+  import_content(filepath)

iris.csv

@@ -0,0 +1,152 @@
+"sepal_length","sepal_width","petal_length","petal_width","Species*"
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+

run.py

@@ -22,6 +22,8 @@ from sklearn.neural_network import MLPClassifier # 1 neural network
 from sklearn.linear_model import LogisticRegression # 1 linear model
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis # 2 discriminant analysis
 from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier # 4 ensemble models
+from joblib import Parallel, delayed
+import multiprocessing
 
 from sklearn.pipeline import make_pipeline
 from sklearn import model_selection
@@ -407,6 +409,7 @@ def callPreResults():
     global XData
     global yData
     global target_names
+    global allParametersPerformancePerModel
 
     DataSpaceResMDS = FunMDS(XData)
     DataSpaceResTSNE = FunTsne(XData)
@@ -425,6 +428,7 @@ def callPreResults():
     preResults.append(json.dumps(AllTargets)) # Position: 4
     preResults.append(json.dumps(DataSpaceResTSNE)) # Position: 5
     preResults.append(json.dumps(DataSpaceUMAP)) # Position: 6
+    preResults.append(json.dumps(allParametersPerformancePerModel)) # Position: 7
 
 # Sending each model's results to frontend
 @app.route('/data/requestDataSpaceResults', methods=["GET", "POST"])
@@ -551,7 +555,7 @@ memory = Memory(location, verbose=0)
 # calculating for all algorithms and models the performance and other results
 @memory.cache
 def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
-    print('test')
+    print('start')
     # instantiate spark session
     spark = (   
         SparkSession    
@@ -632,6 +636,12 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
 
     loop = 10
 
+    # influence calculation for all the instances
+    inputs = range(len(XData))
+    num_cores = multiprocessing.cpu_count()
+    
+    impDataInst = Parallel(n_jobs=num_cores)(delayed(processInput)(i,XData,yData,crossValidation,clf) for i in inputs)
+
     for eachModelParameters in parametersLocalNew:
         clf.set_params(**eachModelParameters)
 
@@ -727,9 +737,11 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
     results.append(PerFeatureAccuracyPandas) # Position: 3 and so on
     results.append(perm_imp_eli5PD) # Position: 4 and so on
     results.append(featureScores) # Position: 5 and so on
+    metrics = metrics.clip(lower=0)
     metrics = metrics.to_json()
     results.append(metrics) # Position: 6 and so on
     results.append(perModelProbPandas) # Position: 7 and so on
+    results.append(json.dumps(impDataInst)) # Position: 8 and so on
 
     return results
 
@@ -749,7 +761,7 @@ def Remove(duplicate):
                 if np.isnan(num):
                     pass
                 else:
-                    final_list.append(int(num)) 
+                    final_list.append(float(num)) 
             else:
                 final_list.append(num) 
     return final_list 
@@ -876,16 +888,16 @@ def UpdateOverview():
 
 def PreprocessingMetrics():
     dicKNN = json.loads(allParametersPerformancePerModel[6])
-    dicSVC = json.loads(allParametersPerformancePerModel[14])
+    dicSVC = json.loads(allParametersPerformancePerModel[15])
-    dicGausNB = json.loads(allParametersPerformancePerModel[22])
+    dicGausNB = json.loads(allParametersPerformancePerModel[24])
-    dicMLP = json.loads(allParametersPerformancePerModel[30])
+    dicMLP = json.loads(allParametersPerformancePerModel[33])
-    dicLR = json.loads(allParametersPerformancePerModel[38])
+    dicLR = json.loads(allParametersPerformancePerModel[42])
-    dicLDA = json.loads(allParametersPerformancePerModel[46])
+    dicLDA = json.loads(allParametersPerformancePerModel[51])
-    dicQDA = json.loads(allParametersPerformancePerModel[54])
+    dicQDA = json.loads(allParametersPerformancePerModel[60])
-    dicRF = json.loads(allParametersPerformancePerModel[62])
+    dicRF = json.loads(allParametersPerformancePerModel[69])
-    dicExtraT = json.loads(allParametersPerformancePerModel[70])
+    dicExtraT = json.loads(allParametersPerformancePerModel[78])
-    dicAdaB = json.loads(allParametersPerformancePerModel[78])
+    dicAdaB = json.loads(allParametersPerformancePerModel[87])
-    dicGradB = json.loads(allParametersPerformancePerModel[86])
+    dicGradB = json.loads(allParametersPerformancePerModel[96])
 
     dfKNN = pd.DataFrame.from_dict(dicKNN)
     dfSVC = pd.DataFrame.from_dict(dicSVC)
@@ -927,16 +939,16 @@ def PreprocessingMetrics():
 
 def PreprocessingPred():
     dicKNN = json.loads(allParametersPerformancePerModel[7])
-    dicSVC = json.loads(allParametersPerformancePerModel[15])
+    dicSVC = json.loads(allParametersPerformancePerModel[16])
-    dicGausNB = json.loads(allParametersPerformancePerModel[23])
+    dicGausNB = json.loads(allParametersPerformancePerModel[25])
-    dicMLP = json.loads(allParametersPerformancePerModel[31])
+    dicMLP = json.loads(allParametersPerformancePerModel[34])
-    dicLR = json.loads(allParametersPerformancePerModel[39])
+    dicLR = json.loads(allParametersPerformancePerModel[43])
-    dicLDA = json.loads(allParametersPerformancePerModel[47])
+    dicLDA = json.loads(allParametersPerformancePerModel[52])
-    dicQDA = json.loads(allParametersPerformancePerModel[55])
+    dicQDA = json.loads(allParametersPerformancePerModel[61])
-    dicRF = json.loads(allParametersPerformancePerModel[63])
+    dicRF = json.loads(allParametersPerformancePerModel[70])
-    dicExtraT = json.loads(allParametersPerformancePerModel[71])
+    dicExtraT = json.loads(allParametersPerformancePerModel[79])
-    dicAdaB = json.loads(allParametersPerformancePerModel[79])
+    dicAdaB = json.loads(allParametersPerformancePerModel[88])
-    dicGradB = json.loads(allParametersPerformancePerModel[87])
+    dicGradB = json.loads(allParametersPerformancePerModel[97])
 
     dfKNN = pd.DataFrame.from_dict(dicKNN)
     dfSVC = pd.DataFrame.from_dict(dicSVC)
@@ -986,20 +998,19 @@ def PreprocessingPredUpdate(Models):
     Models = json.loads(Models)
     ModelsList= []
     for loop in Models['ClassifiersList']:
-        temp = [int(s) for s in re.findall(r'\b\d+\b', loop)]
+        ModelsList.append(loop)
-        ModelsList.append(temp[0])
 
     dicKNN = json.loads(allParametersPerformancePerModel[7])
-    dicSVC = json.loads(allParametersPerformancePerModel[15])
+    dicSVC = json.loads(allParametersPerformancePerModel[16])
-    dicGausNB = json.loads(allParametersPerformancePerModel[23])
+    dicGausNB = json.loads(allParametersPerformancePerModel[25])
-    dicMLP = json.loads(allParametersPerformancePerModel[31])
+    dicMLP = json.loads(allParametersPerformancePerModel[34])
-    dicLR = json.loads(allParametersPerformancePerModel[39])
+    dicLR = json.loads(allParametersPerformancePerModel[43])
-    dicLDA = json.loads(allParametersPerformancePerModel[47])
+    dicLDA = json.loads(allParametersPerformancePerModel[52])
-    dicQDA = json.loads(allParametersPerformancePerModel[55])
+    dicQDA = json.loads(allParametersPerformancePerModel[61])
-    dicRF = json.loads(allParametersPerformancePerModel[63])
+    dicRF = json.loads(allParametersPerformancePerModel[70])
-    dicExtraT = json.loads(allParametersPerformancePerModel[71])
+    dicExtraT = json.loads(allParametersPerformancePerModel[79])
-    dicAdaB = json.loads(allParametersPerformancePerModel[79])
+    dicAdaB = json.loads(allParametersPerformancePerModel[88])
-    dicGradB = json.loads(allParametersPerformancePerModel[87])
+    dicGradB = json.loads(allParametersPerformancePerModel[97])
 
     dfKNN = pd.DataFrame.from_dict(dicKNN)
     dfSVC = pd.DataFrame.from_dict(dicSVC)
@@ -1047,8 +1058,7 @@ def PreprocessingPredUpdate(Models):
             df_concatProbs = df_concatProbs.drop(df_concatProbs.index[index])
             deletedElements = deletedElements + 1
     df_concatProbsCleared = df_concatProbs
-    listIDsRemaining = df_concatProbsCleared.index.values.tolist()
+    listIDsRemoved = df_concatProbsCleared.index.values.tolist()
-
     predictionsAll = PreprocessingPred()
     PredictionSpaceAll = FunMDS(predictionsAll)
 
@@ -1059,31 +1069,27 @@ def PreprocessingPredUpdate(Models):
 
     PredictionSpaceSel = FunMDS(predictionsSel)
 
-    #ModelSpaceMDSNewComb = [list(a) for a in  zip(PredictionSpaceAll[0], ModelSpaceMDS[1])]
-
-    #ModelSpaceMDSNewSel = FunMDS(df_concatMetrics)
-    #ModelSpaceMDSNewSelComb = [list(a) for a in  zip(ModelSpaceMDSNewSel[0], ModelSpaceMDSNewSel[1])]
-
     mtx2PredFinal = []
     mtx1Pred, mtx2Pred, disparity2 = procrustes(PredictionSpaceAll, PredictionSpaceSel)
 
-    a1, b1 = zip(*mtx2Pred)
+    a1 = [i[1] for i in mtx2Pred]
+    b1 = [i[0] for i in mtx2Pred]
     mtx2PredFinal.append(a1)
     mtx2PredFinal.append(b1)
-    return [mtx2PredFinal,listIDsRemaining]
+    return [mtx2PredFinal,listIDsRemoved]
 
 def PreprocessingParam():
     dicKNN = json.loads(allParametersPerformancePerModel[1])
-    dicSVC = json.loads(allParametersPerformancePerModel[9])
+    dicSVC = json.loads(allParametersPerformancePerModel[10])
-    dicGausNB = json.loads(allParametersPerformancePerModel[17])
+    dicGausNB = json.loads(allParametersPerformancePerModel[19])
-    dicMLP = json.loads(allParametersPerformancePerModel[25])
+    dicMLP = json.loads(allParametersPerformancePerModel[28])
-    dicLR = json.loads(allParametersPerformancePerModel[33])
+    dicLR = json.loads(allParametersPerformancePerModel[37])
-    dicLDA = json.loads(allParametersPerformancePerModel[41])
+    dicLDA = json.loads(allParametersPerformancePerModel[46])
-    dicQDA = json.loads(allParametersPerformancePerModel[49])
+    dicQDA = json.loads(allParametersPerformancePerModel[55])
-    dicRF = json.loads(allParametersPerformancePerModel[57])
+    dicRF = json.loads(allParametersPerformancePerModel[64])
-    dicExtraT = json.loads(allParametersPerformancePerModel[65])
+    dicExtraT = json.loads(allParametersPerformancePerModel[73])
-    dicAdaB = json.loads(allParametersPerformancePerModel[73])
+    dicAdaB = json.loads(allParametersPerformancePerModel[82])
-    dicGradB = json.loads(allParametersPerformancePerModel[81])
+    dicGradB = json.loads(allParametersPerformancePerModel[91])
 
     dicKNN = dicKNN['params']
     dicSVC = dicSVC['params']
@@ -1150,16 +1156,16 @@ def PreprocessingParam():
 
 def PreprocessingParamSep():
     dicKNN = json.loads(allParametersPerformancePerModel[1])
-    dicSVC = json.loads(allParametersPerformancePerModel[9])
+    dicSVC = json.loads(allParametersPerformancePerModel[10])
-    dicGausNB = json.loads(allParametersPerformancePerModel[17])
+    dicGausNB = json.loads(allParametersPerformancePerModel[19])
-    dicMLP = json.loads(allParametersPerformancePerModel[25])
+    dicMLP = json.loads(allParametersPerformancePerModel[28])
-    dicLR = json.loads(allParametersPerformancePerModel[33])
+    dicLR = json.loads(allParametersPerformancePerModel[37])
-    dicLDA = json.loads(allParametersPerformancePerModel[41])
+    dicLDA = json.loads(allParametersPerformancePerModel[46])
-    dicQDA = json.loads(allParametersPerformancePerModel[49])
+    dicQDA = json.loads(allParametersPerformancePerModel[55])
-    dicRF = json.loads(allParametersPerformancePerModel[57])
+    dicRF = json.loads(allParametersPerformancePerModel[64])
-    dicExtraT = json.loads(allParametersPerformancePerModel[65])
+    dicExtraT = json.loads(allParametersPerformancePerModel[73])
-    dicAdaB = json.loads(allParametersPerformancePerModel[73])
+    dicAdaB = json.loads(allParametersPerformancePerModel[82])
-    dicGradB = json.loads(allParametersPerformancePerModel[81])
+    dicGradB = json.loads(allParametersPerformancePerModel[91])
 
     dicKNN = dicKNN['params']
     dicSVC = dicSVC['params']
@@ -1225,16 +1231,16 @@ def PreprocessingParamSep():
 
 def preProcessPerClassM():
     dicKNN = json.loads(allParametersPerformancePerModel[2])
-    dicSVC = json.loads(allParametersPerformancePerModel[10])
+    dicSVC = json.loads(allParametersPerformancePerModel[11])
-    dicGausNB = json.loads(allParametersPerformancePerModel[18])
+    dicGausNB = json.loads(allParametersPerformancePerModel[20])
-    dicMLP = json.loads(allParametersPerformancePerModel[26])
+    dicMLP = json.loads(allParametersPerformancePerModel[29])
-    dicLR = json.loads(allParametersPerformancePerModel[34])
+    dicLR = json.loads(allParametersPerformancePerModel[38])
-    dicLDA = json.loads(allParametersPerformancePerModel[42])
+    dicLDA = json.loads(allParametersPerformancePerModel[47])
-    dicQDA = json.loads(allParametersPerformancePerModel[50])
+    dicQDA = json.loads(allParametersPerformancePerModel[56])
-    dicRF = json.loads(allParametersPerformancePerModel[58])
+    dicRF = json.loads(allParametersPerformancePerModel[65])
-    dicExtraT = json.loads(allParametersPerformancePerModel[66])
+    dicExtraT = json.loads(allParametersPerformancePerModel[74])
-    dicAdaB = json.loads(allParametersPerformancePerModel[74])
+    dicAdaB = json.loads(allParametersPerformancePerModel[83])
-    dicGradB = json.loads(allParametersPerformancePerModel[82])
+    dicGradB = json.loads(allParametersPerformancePerModel[92])
 
     dfKNN = pd.DataFrame.from_dict(dicKNN)
     dfSVC = pd.DataFrame.from_dict(dicSVC)
@@ -1277,16 +1283,16 @@ def preProcessPerClassM():
 
 def preProcessFeatAcc():
     dicKNN = json.loads(allParametersPerformancePerModel[3])
-    dicSVC = json.loads(allParametersPerformancePerModel[11])
+    dicSVC = json.loads(allParametersPerformancePerModel[12])
-    dicGausNB = json.loads(allParametersPerformancePerModel[19])
+    dicGausNB = json.loads(allParametersPerformancePerModel[21])
-    dicMLP = json.loads(allParametersPerformancePerModel[27])
+    dicMLP = json.loads(allParametersPerformancePerModel[30])
-    dicLR = json.loads(allParametersPerformancePerModel[35])
+    dicLR = json.loads(allParametersPerformancePerModel[39])
-    dicLDA = json.loads(allParametersPerformancePerModel[43])
+    dicLDA = json.loads(allParametersPerformancePerModel[48])
-    dicQDA = json.loads(allParametersPerformancePerModel[51])
+    dicQDA = json.loads(allParametersPerformancePerModel[57])
-    dicRF = json.loads(allParametersPerformancePerModel[59])
+    dicRF = json.loads(allParametersPerformancePerModel[66])
-    dicExtraT = json.loads(allParametersPerformancePerModel[67])
+    dicExtraT = json.loads(allParametersPerformancePerModel[75])
-    dicAdaB = json.loads(allParametersPerformancePerModel[75])
+    dicAdaB = json.loads(allParametersPerformancePerModel[84])
-    dicGradB = json.loads(allParametersPerformancePerModel[83])
+    dicGradB = json.loads(allParametersPerformancePerModel[93])
 
     dfKNN = pd.DataFrame.from_dict(dicKNN)
     dfSVC = pd.DataFrame.from_dict(dicSVC)
@@ -1329,16 +1335,16 @@ def preProcessFeatAcc():
 
 def preProcessPerm():
     dicKNN = json.loads(allParametersPerformancePerModel[4])
-    dicSVC = json.loads(allParametersPerformancePerModel[12])
+    dicSVC = json.loads(allParametersPerformancePerModel[13])
-    dicGausNB = json.loads(allParametersPerformancePerModel[20])
+    dicGausNB = json.loads(allParametersPerformancePerModel[22])
-    dicMLP = json.loads(allParametersPerformancePerModel[28])
+    dicMLP = json.loads(allParametersPerformancePerModel[31])
-    dicLR = json.loads(allParametersPerformancePerModel[36])
+    dicLR = json.loads(allParametersPerformancePerModel[40])
-    dicLDA = json.loads(allParametersPerformancePerModel[44])
+    dicLDA = json.loads(allParametersPerformancePerModel[49])
-    dicQDA = json.loads(allParametersPerformancePerModel[52])
+    dicQDA = json.loads(allParametersPerformancePerModel[58])
-    dicRF = json.loads(allParametersPerformancePerModel[60])
+    dicRF = json.loads(allParametersPerformancePerModel[67])
-    dicExtraT = json.loads(allParametersPerformancePerModel[68])
+    dicExtraT = json.loads(allParametersPerformancePerModel[76])
-    dicAdaB = json.loads(allParametersPerformancePerModel[76])
+    dicAdaB = json.loads(allParametersPerformancePerModel[85])
-    dicGradB = json.loads(allParametersPerformancePerModel[84])
+    dicGradB = json.loads(allParametersPerformancePerModel[94])
 
     dfKNN = pd.DataFrame.from_dict(dicKNN)
     dfSVC = pd.DataFrame.from_dict(dicSVC)
@@ -1388,6 +1394,9 @@ def preProcessFeatSc():
 def preProcsumPerMetric(factors):
     sumPerClassifier = []
     loopThroughMetrics = PreprocessingMetrics()
+    loopThroughMetrics.loc[:, 'mean_test_neg_mean_absolute_error'] = loopThroughMetrics.loc[:, 'mean_test_neg_mean_absolute_error'] + 1
+    loopThroughMetrics.loc[:, 'mean_test_neg_root_mean_squared_error'] = loopThroughMetrics.loc[:, 'mean_test_neg_root_mean_squared_error'] + 1
+    loopThroughMetrics.loc[:, 'log_loss'] = 1 - loopThroughMetrics.loc[:, 'log_loss']
     for row in loopThroughMetrics.iterrows():
         rowSum = 0
         name, values = row
@@ -1396,44 +1405,45 @@ def preProcsumPerMetric(factors):
         if sum(factors) is 0:
             sumPerClassifier = 0
         else:
-            sumPerClassifier.append(rowSum/sum(factors))
+            sumPerClassifier.append(rowSum/sum(factors) * 100)
     return sumPerClassifier
 
 def preProcMetricsAllAndSel():
     loopThroughMetrics = PreprocessingMetrics()
     global factors
     metricsPerModelColl = []
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_accuracy'].sum()/loopThroughMetrics['mean_test_accuracy'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_accuracy'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_neg_mean_absolute_error'].sum()/loopThroughMetrics['mean_test_neg_mean_absolute_error'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_neg_mean_absolute_error'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_neg_root_mean_squared_error'].sum()/loopThroughMetrics['mean_test_neg_root_mean_squared_error'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_neg_root_mean_squared_error'])
-    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_micro'].sum()/loopThroughMetrics['geometric_mean_score_micro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_micro'])
-    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_macro'].sum()/loopThroughMetrics['geometric_mean_score_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_macro'])
-    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_weighted'].sum()/loopThroughMetrics['geometric_mean_score_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_micro'].sum()/loopThroughMetrics['mean_test_precision_micro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_micro'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_macro'].sum()/loopThroughMetrics['mean_test_precision_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_macro'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_weighted'].sum()/loopThroughMetrics['mean_test_precision_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_micro'].sum()/loopThroughMetrics['mean_test_recall_micro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_micro'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_macro'].sum()/loopThroughMetrics['mean_test_recall_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_macro'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_weighted'].sum()/loopThroughMetrics['mean_test_recall_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['f5_micro'].sum()/loopThroughMetrics['f5_micro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f5_micro'])
-    metricsPerModelColl.append(loopThroughMetrics['f5_macro'].sum()/loopThroughMetrics['f5_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f5_macro'])
-    metricsPerModelColl.append(loopThroughMetrics['f5_weighted'].sum()/loopThroughMetrics['f5_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f5_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['f1_micro'].sum()/loopThroughMetrics['f1_micro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f1_micro'])
-    metricsPerModelColl.append(loopThroughMetrics['f1_macro'].sum()/loopThroughMetrics['f1_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f1_macro'])
-    metricsPerModelColl.append(loopThroughMetrics['f1_weighted'].sum()/loopThroughMetrics['f1_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f1_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['f2_micro'].sum()/loopThroughMetrics['f2_micro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f2_micro'])
-    metricsPerModelColl.append(loopThroughMetrics['f2_macro'].sum()/loopThroughMetrics['f2_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f2_macro'])
-    metricsPerModelColl.append(loopThroughMetrics['f2_weighted'].sum()/loopThroughMetrics['f2_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f2_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['matthews_corrcoef'].sum()/loopThroughMetrics['matthews_corrcoef'].count())
+    metricsPerModelColl.append(loopThroughMetrics['matthews_corrcoef'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_roc_auc_ovo_weighted'].sum()/loopThroughMetrics['mean_test_roc_auc_ovo_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_roc_auc_ovo_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['log_loss'].sum()/loopThroughMetrics['log_loss'].count())
+    metricsPerModelColl.append(loopThroughMetrics['log_loss'])
     for index, metric in enumerate(metricsPerModelColl):
         if (index == 1 or index == 2):
-            metricsPerModelColl[index] = (metric + 1)*factors[index]
+            metricsPerModelColl[index] = ((metric + 1)*factors[index]) * 100
         elif (index == 23):
-            metricsPerModelColl[index] = (1 - metric)*factors[index]
+            metricsPerModelColl[index] = ((1 - metric)*factors[index] ) * 100
         else:  
-            metricsPerModelColl[index] = metric*factors[index]
+            metricsPerModelColl[index] = (metric*factors[index]) * 100
+        metricsPerModelColl[index] = metricsPerModelColl[index].to_json()
     return metricsPerModelColl
 
 def preProceModels():
@@ -1580,22 +1590,60 @@ def ComputeMetricsForSel(Models):
     MetricsAlltoSel = PreprocessingMetrics()
     listofModels = []
     for loop in Models['ClassifiersList']:
-        temp = [int(s) for s in re.findall(r'\b\d+\b', loop)]
+        listofModels.append(loop)
-        listofModels.append(temp[0])
     MetricsAlltoSel = MetricsAlltoSel.loc[listofModels,:]
 
     global metricsPerModelCollSel
+    global factors
     metricsPerModelCollSel = []
-    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_accuracy'].sum()/MetricsAlltoSel['mean_test_accuracy'].count())
+    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_accuracy'])
-    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_f1_macro'].sum()/MetricsAlltoSel['mean_test_f1_macro'].count())
+    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_neg_mean_absolute_error'])
-    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_precision'].sum()/MetricsAlltoSel['mean_test_precision'].count())
+    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_neg_root_mean_squared_error'])
-    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_recall'].sum()/MetricsAlltoSel['mean_test_recall'].count())
+    metricsPerModelCollSel.append(MetricsAlltoSel['geometric_mean_score_micro'])
-    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_jaccard'].sum()/MetricsAlltoSel['mean_test_jaccard'].count())
+    metricsPerModelCollSel.append(MetricsAlltoSel['geometric_mean_score_macro'])
-    for index, metric in enumerate(metricsPerModelCollSel):
+    metricsPerModelCollSel.append(MetricsAlltoSel['geometric_mean_score_weighted'])
-        metricsPerModelCollSel[index] = metric*factors[index]
+    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_precision_micro'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_precision_macro'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_precision_weighted'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_recall_micro'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_recall_macro'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_recall_weighted'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['f5_micro'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['f5_macro'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['f5_weighted'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['f1_micro'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['f1_macro'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['f1_weighted'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['f2_micro'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['f2_macro'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['f2_weighted'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['matthews_corrcoef'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['mean_test_roc_auc_ovo_weighted'])
+    metricsPerModelCollSel.append(MetricsAlltoSel['log_loss'])
 
+    for index, metric in enumerate(metricsPerModelCollSel):
+        if (index == 1 or index == 2):
+            metricsPerModelCollSel[index] = (metric + 1)*factors[index]
+        elif (index == 23):
+            metricsPerModelCollSel[index] = (1 - metric)*factors[index]
+        else:  
+            metricsPerModelCollSel[index] = metric*factors[index]
+        metricsPerModelCollSel[index] = metricsPerModelCollSel[index].to_json()
     return 'okay'
 
+# function to get unique values 
+def unique(list1): 
+  
+    # intilize a null list 
+    unique_list = [] 
+      
+    # traverse for all elements 
+    for x in list1: 
+        # check if exists in unique_list or not 
+        if x not in unique_list: 
+            unique_list.append(x) 
+    return unique_list
+
 # Sending the overview classifiers' results to be visualized as a scatterplot
 @app.route('/data/BarChartSelectedModels', methods=["GET", "POST"])
 def SendToUpdateBarChart():
@@ -1615,6 +1663,7 @@ def RetrieveSelDataPoints():
     for loop in DataPointsSelClear['DataPointsSel']:
         temp = [int(s) for s in re.findall(r'\b\d+\b', loop)]
         listofDataPoints.append(temp[0])
+    global algorithmsList
 
     paramsListSepPD = []
     paramsListSepPD = PreprocessingParamSep()
@@ -1644,7 +1693,6 @@ def RetrieveSelDataPoints():
         withoutDuplicates = Remove(value)
         RetrieveParamsCleared[key] = withoutDuplicates
     RetrieveParamsClearedListSVC.append(RetrieveParamsCleared)
-    
     RetrieveParamsCleared = {}
     RetrieveParamsClearedListGausNB = []
     for key, value in paramsListSeptoDicGausNB.items():
@@ -1740,7 +1788,6 @@ def RetrieveSelDataPoints():
 
     if (len(paramsListSeptoDicGradB['n_estimators']) is 0):
         RetrieveParamsClearedListGradB = []
-    
     for eachAlgor in algorithms:
         if (eachAlgor) == 'KNN':
             clf = KNeighborsClassifier()
@@ -1787,8 +1834,7 @@ def RetrieveSelDataPoints():
             params = RetrieveParamsClearedListGradB
             AlgorithmsIDsEnd = GradBModelsCount
         metricsSelList = GridSearchSel(clf, params, factors, AlgorithmsIDsEnd, listofDataPoints)
-
+    if (len(metricsSelList[0]) != 0 and len(metricsSelList[1]) != 0 and len(metricsSelList[2]) != 0 and len(metricsSelList[3]) != 0 and len(metricsSelList[4]) != 0 and len(metricsSelList[5]) != 0 and len(metricsSelList[6]) != 0 and len(metricsSelList[7]) != 0 and len(metricsSelList[8]) != 0 and len(metricsSelList[9]) != 0 and len(metricsSelList[10]) != 0):
-    if (len(metricsSelList[0]) != 0 and len(metricsSelList[1]) != 0 and len(metricsSelList[2]) != 0 and len(metricsSelList[3]) != 0 and len(metricsSelList[4]) != 0 and len(metricsSelList[5]) != 0 and len(metricsSelList[6]) != 0 and len(metricsSelList[7]) != 0 and len(metricsSelList[8]) != 0 and len(metricsSelList[9]) != 0 and len(metricsSelList[10])):
         dicKNN = json.loads(metricsSelList[0])
         dfKNN = pd.DataFrame.from_dict(dicKNN)
         parametersSelDataPD = parametersSelData[0].apply(pd.Series)
@@ -2023,10 +2069,12 @@ def RetrieveSelDataPoints():
                 dfAdaBCleared = dfGradB.drop(dfGradB.index[set_diff_df])
             df_concatMetrics = dfAdaBCleared
     
+    df_concatMetrics.loc[:, 'mean_test_neg_mean_absolute_error'] = df_concatMetrics.loc[:, 'mean_test_neg_mean_absolute_error'] + 1
+    df_concatMetrics.loc[:, 'mean_test_neg_root_mean_squared_error'] = df_concatMetrics.loc[:, 'mean_test_neg_root_mean_squared_error'] + 1
+    df_concatMetrics.loc[:, 'log_loss'] = 1 - df_concatMetrics.loc[:, 'log_loss']
     global sumPerClassifierSelUpdate
     sumPerClassifierSelUpdate = []
     sumPerClassifierSelUpdate = preProcsumPerMetricAccordingtoData(factors, df_concatMetrics)
-
     ModelSpaceMDSNewComb = [list(a) for a in  zip(ModelSpaceMDS[0], ModelSpaceMDS[1])]
 
     ModelSpaceMDSNewSel = FunMDS(df_concatMetrics)
@@ -2195,15 +2243,13 @@ def preProcsumPerMetricAccordingtoData(factors, loopThroughMetrics):
     sumPerClassifier = []
     for row in loopThroughMetrics.iterrows():
         rowSum = 0
-        lengthFactors = len(scoring)
         name, values = row
         for loop, elements in enumerate(values):
-            lengthFactors = lengthFactors -  1 + factors[loop]
             rowSum = elements*factors[loop] + rowSum
-        if lengthFactors is 0:
+        if sum(factors) is 0:
             sumPerClassifier = 0
         else:
-            sumPerClassifier.append(rowSum/lengthFactors)
+            sumPerClassifier.append(rowSum/sum(factors) * 100)
     return sumPerClassifier
 
 # Sending the overview classifiers' results to be visualized as a scatterplot
@@ -2242,6 +2288,7 @@ def EnsembleModel(Models, keyRetrieved):
 
     global XData
     global yData
+    global sclf
 
     lr = LogisticRegression()
 
@@ -2258,75 +2305,75 @@ def EnsembleModel(Models, keyRetrieved):
             arg = dfParamKNNFilt[eachelem]
             all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), KNeighborsClassifier().set_params(**arg)))
     
-        temp = json.loads(allParametersPerformancePerModel[9])
+        temp = json.loads(allParametersPerformancePerModel[10])
         dfParamSVC = pd.DataFrame.from_dict(temp)
         dfParamSVCFilt = dfParamSVC.iloc[:,0]
         for eachelem in SVCModels:
             arg = dfParamSVCFilt[eachelem-SVCModelsCount]
-            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), SVC(probability=True).set_params(**arg)))
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), SVC(probability=True,random_state=RANDOM_SEED).set_params(**arg)))
         
-        temp = json.loads(allParametersPerformancePerModel[17])
+        temp = json.loads(allParametersPerformancePerModel[19])
         dfParamGauNB = pd.DataFrame.from_dict(temp)
         dfParamGauNBFilt = dfParamGauNB.iloc[:,0]
         for eachelem in GausNBModels:
             arg = dfParamGauNBFilt[eachelem-GausNBModelsCount]
             all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), GaussianNB().set_params(**arg)))
 
-        temp = json.loads(allParametersPerformancePerModel[25])
+        temp = json.loads(allParametersPerformancePerModel[28])
         dfParamMLP = pd.DataFrame.from_dict(temp)
         dfParamMLPFilt = dfParamMLP.iloc[:,0]
         for eachelem in MLPModels:
             arg = dfParamMLPFilt[eachelem-MLPModelsCount]
-            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), MLPClassifier().set_params(**arg)))
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), MLPClassifier(random_state=RANDOM_SEED).set_params(**arg)))
 
-        temp = json.loads(allParametersPerformancePerModel[33])
+        temp = json.loads(allParametersPerformancePerModel[37])
         dfParamLR = pd.DataFrame.from_dict(temp)
         dfParamLRFilt = dfParamLR.iloc[:,0]
         for eachelem in LRModels:
             arg = dfParamLRFilt[eachelem-LRModelsCount]
-            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), LogisticRegression().set_params(**arg)))
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), LogisticRegression(random_state=RANDOM_SEED).set_params(**arg)))
 
-        temp = json.loads(allParametersPerformancePerModel[41])
+        temp = json.loads(allParametersPerformancePerModel[46])
         dfParamLDA = pd.DataFrame.from_dict(temp)
         dfParamLDAFilt = dfParamLDA.iloc[:,0]
         for eachelem in LDAModels:
             arg = dfParamLDAFilt[eachelem-LDAModelsCount]
-            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), LinearDiscriminantAnalysis().set_params(**arg)))
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), LinearDiscriminantAnalysis(random_state=RANDOM_SEED).set_params(**arg)))
 
-        temp = json.loads(allParametersPerformancePerModel[49])
+        temp = json.loads(allParametersPerformancePerModel[55])
         dfParamQDA = pd.DataFrame.from_dict(temp)
         dfParamQDAFilt = dfParamQDA.iloc[:,0]
         for eachelem in QDAModels:
             arg = dfParamQDAFilt[eachelem-QDAModelsCount]
             all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), QuadraticDiscriminantAnalysis().set_params(**arg)))
 
-        temp = json.loads(allParametersPerformancePerModel[57])
+        temp = json.loads(allParametersPerformancePerModel[64])
         dfParamRF = pd.DataFrame.from_dict(temp)
         dfParamRFFilt = dfParamRF.iloc[:,0]
         for eachelem in RFModels:
             arg = dfParamRFFilt[eachelem-RFModelsCount]
-            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), RandomForestClassifier().set_params(**arg)))
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), RandomForestClassifier(random_state=RANDOM_SEED).set_params(**arg)))
 
-        temp = json.loads(allParametersPerformancePerModel[65])
+        temp = json.loads(allParametersPerformancePerModel[73])
         dfParamExtraT = pd.DataFrame.from_dict(temp)
         dfParamExtraTFilt = dfParamExtraT.iloc[:,0]
         for eachelem in ExtraTModels:
             arg = dfParamExtraTFilt[eachelem-ExtraTModelsCount]
-            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), ExtraTreesClassifier().set_params(**arg)))
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), ExtraTreesClassifier(random_state=RANDOM_SEED).set_params(**arg)))
 
-        temp = json.loads(allParametersPerformancePerModel[73])
+        temp = json.loads(allParametersPerformancePerModel[82])
         dfParamAdaB = pd.DataFrame.from_dict(temp)
         dfParamAdaBFilt = dfParamAdaB.iloc[:,0]
         for eachelem in AdaBModels:
             arg = dfParamAdaBFilt[eachelem-AdaBModelsCount]
-            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), AdaBoostClassifier().set_params(**arg)))
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), AdaBoostClassifier(random_state=RANDOM_SEED).set_params(**arg)))
 
-        temp = json.loads(allParametersPerformancePerModel[81])
+        temp = json.loads(allParametersPerformancePerModel[91])
         dfParamGradB = pd.DataFrame.from_dict(temp)
         dfParamGradBFilt = dfParamGradB.iloc[:,0]
         for eachelem in GradBModels:
             arg = dfParamGradBFilt[eachelem-GradBModelsCount]
-            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), GradientBoostingClassifier().set_params(**arg)))
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), GradientBoostingClassifier(random_state=RANDOM_SEED).set_params(**arg)))
 
         global sclfStack
         sclfStack = 0
@@ -2345,8 +2392,7 @@ def EnsembleModel(Models, keyRetrieved):
         for index, modHere in enumerate(ModelsAll):
             flag = 0
             for loop in Models['ClassifiersList']:
-                temp = [int(s) for s in re.findall(r'\b\d+\b', loop)]
+                if (int(loop) == int(modHere)):
-                if (int(temp[0]) == int(modHere)):
                     flag = 1
             if (flag is 1):
                 all_classifiersSelection.append(all_classifiers[index])
@@ -2376,12 +2422,12 @@ def EnsembleModel(Models, keyRetrieved):
                 store = 0  
             else:
                 store = store + 1          
-            temp = json.loads(allParametersPerformancePerModel[9])
+            temp = json.loads(allParametersPerformancePerModel[10])
             dfParamSVC = pd.DataFrame.from_dict(temp)
             dfParamSVCFilt = dfParamSVC.iloc[:,0]
             for index, eachelem in enumerate(SVCModels):
                 arg = dfParamRFFilt[eachelem-SVCModelsCount]
-                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), SVC(probability=True).set_params(**arg)))
+                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), SVC(probability=True,random_state=RANDOM_SEED).set_params(**arg)))
                 store = index
                 flag = 1
 
@@ -2389,7 +2435,7 @@ def EnsembleModel(Models, keyRetrieved):
                 store = 0  
             else:
                 store = store + 1        
-            temp = json.loads(allParametersPerformancePerModel[17])
+            temp = json.loads(allParametersPerformancePerModel[19])
             dfParamGauNB = pd.DataFrame.from_dict(temp)
             dfParamGauNBFilt = dfParamGauNB.iloc[:,0]
             for index, eachelem in enumerate(GausNBModels):
@@ -2402,12 +2448,12 @@ def EnsembleModel(Models, keyRetrieved):
                 store = 0  
             else:
                 store = store + 1   
-            temp = json.loads(allParametersPerformancePerModel[25])
+            temp = json.loads(allParametersPerformancePerModel[28])
             dfParamMLP = pd.DataFrame.from_dict(temp)
             dfParamMLPFilt = dfParamMLP.iloc[:,0]
             for index, eachelem in enumerate(MLPModels):
                 arg = dfParamMLPFilt[eachelem-MLPModelsCount]
-                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), MLPClassifier().set_params(**arg)))
+                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), MLPClassifier(random_state=RANDOM_SEED).set_params(**arg)))
                 store = index
                 flag = 1
 
@@ -2415,12 +2461,12 @@ def EnsembleModel(Models, keyRetrieved):
                 store = 0  
             else:
                 store = store + 1   
-            temp = json.loads(allParametersPerformancePerModel[33])
+            temp = json.loads(allParametersPerformancePerModel[37])
             dfParamLR = pd.DataFrame.from_dict(temp)
             dfParamLRFilt = dfParamLR.iloc[:,0]
             for index, eachelem in enumerate(LRModels):
                 arg = dfParamLRFilt[eachelem-LRModelsCount]
-                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), LogisticRegression().set_params(**arg)))
+                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), LogisticRegression(random_state=RANDOM_SEED).set_params(**arg)))
                 store = index
                 flag = 1
 
@@ -2428,12 +2474,12 @@ def EnsembleModel(Models, keyRetrieved):
                 store = 0  
             else:
                 store = store + 1   
-            temp = json.loads(allParametersPerformancePerModel[41])
+            temp = json.loads(allParametersPerformancePerModel[46])
             dfParamLDA = pd.DataFrame.from_dict(temp)
             dfParamLDAFilt = dfParamLDA.iloc[:,0]
             for index, eachelem in enumerate(LDAModels):
                 arg = dfParamLDAFilt[eachelem-LDAModelsCount]
-                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), LinearDiscriminantAnalysis().set_params(**arg)))
+                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), LinearDiscriminantAnalysis(random_state=RANDOM_SEED).set_params(**arg)))
                 store = index
                 flag = 1
 
@@ -2441,7 +2487,7 @@ def EnsembleModel(Models, keyRetrieved):
                 store = 0  
             else:
                 store = store + 1   
-            temp = json.loads(allParametersPerformancePerModel[49])
+            temp = json.loads(allParametersPerformancePerModel[55])
             dfParamQDA = pd.DataFrame.from_dict(temp)
             dfParamQDAFilt = dfParamQDA.iloc[:,0]
             for index, eachelem in enumerate(QDAModels):
@@ -2454,12 +2500,12 @@ def EnsembleModel(Models, keyRetrieved):
                 store = 0  
             else:
                 store = store + 1   
-            temp = json.loads(allParametersPerformancePerModel[57])
+            temp = json.loads(allParametersPerformancePerModel[64])
             dfParamRF = pd.DataFrame.from_dict(temp)
             dfParamRFFilt = dfParamRF.iloc[:,0]
             for index, eachelem in enumerate(RFModels):
                 arg = dfParamRFFilt[eachelem-RFModelsCount]
-                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), RandomForestClassifier().set_params(**arg)))
+                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), RandomForestClassifier(random_state=RANDOM_SEED).set_params(**arg)))
                 store = index
                 flag = 1
 
@@ -2467,12 +2513,12 @@ def EnsembleModel(Models, keyRetrieved):
                 store = 0  
             else:
                 store = store + 1   
-            temp = json.loads(allParametersPerformancePerModel[65])
+            temp = json.loads(allParametersPerformancePerModel[73])
             dfParamExtraT = pd.DataFrame.from_dict(temp)
             dfParamExtraTFilt = dfParamExtraT.iloc[:,0]
             for index, eachelem in enumerate(ExtraTModels):
                 arg = dfParamExtraTFilt[eachelem-ExtraTModelsCount]
-                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), ExtraTreesClassifier().set_params(**arg)))
+                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), ExtraTreesClassifier(random_state=RANDOM_SEED).set_params(**arg)))
                 store = index
                 flag = 1
 
@@ -2480,12 +2526,12 @@ def EnsembleModel(Models, keyRetrieved):
                 store = 0  
             else:
                 store = store + 1   
-            temp = json.loads(allParametersPerformancePerModel[73])
+            temp = json.loads(allParametersPerformancePerModel[82])
             dfParamAdaB = pd.DataFrame.from_dict(temp)
             dfParamAdaBFilt = dfParamAdaB.iloc[:,0]
             for index, eachelem in enumerate(AdaBModels):
                 arg = dfParamAdaBFilt[eachelem-AdaBModelsCount]
-                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), AdaBoostClassifier().set_params(**arg)))
+                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), AdaBoostClassifier(random_state=RANDOM_SEED).set_params(**arg)))
                 store = index
                 flag = 1
 
@@ -2493,7 +2539,7 @@ def EnsembleModel(Models, keyRetrieved):
                 store = 0  
             else:
                 store = store + 1   
-            temp = json.loads(allParametersPerformancePerModel[81])
+            temp = json.loads(allParametersPerformancePerModel[91])
             dfParamGradB = pd.DataFrame.from_dict(temp)
             dfParamGradBFilt = dfParamGradB.iloc[:,0]
             for index, eachelem in enumerate(GradBModels):
@@ -2513,10 +2559,9 @@ def EnsembleModel(Models, keyRetrieved):
         for index, modHere in enumerate(ModelsAll):
             flag = 0
             for loop in Models['ClassifiersList']:
-                temp = [int(s) for s in re.findall(r'\b\d+\b', loop)]
+                if (int(loop) == int(modHere)):
-                if (int(temp[0]) == int(modHere)):
                     flag = 1
-            if (flag is 0):
+            if (flag is 1):
                 all_classifiersSelection.append(all_classifiers[index])
 
         sclfStack = StackingCVClassifier(classifiers=all_classifiersSelection,
@@ -2525,6 +2570,7 @@ def EnsembleModel(Models, keyRetrieved):
                             random_state=RANDOM_SEED,
                             n_jobs = -1)
         
+        
         #else:
         #    for index, eachelem in enumerate(algorithmsWithoutDuplicates):
         #        if (eachelem == 'KNN'):
@@ -2541,41 +2587,31 @@ def EnsembleModel(Models, keyRetrieved):
         #                        random_state=RANDOM_SEED,
         #                        n_jobs = -1)
     
-    # parallelize all that 
+    num_cores = multiprocessing.cpu_count()
-    temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring='accuracy', n_jobs=-1)
+    inputsSc = ['accuracy','precision_weighted','recall_weighted','accuracy','precision_weighted','recall_weighted']
-    scores.append(temp.mean())
+    flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(sclf,sclfStack,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
-    scores.append(temp.std())
+    scores = [item for sublist in flat_results for item in sublist]
-
-    # influence calculation for all the instances
-    #global DataHeatmap
-    #DataHeatmap = []
-
-    #for indexValue, row in XData.iterrows():
-    #    XDataRemove = XData.copy()
-    #    XDataRemove.drop(indexValue, inplace=True)
-    #    yDataRemove = yData.copy()
-    #    del yDataRemove[indexValue]
-    #    tempRemove = model_selection.cross_val_score(sclf, XDataRemove, yDataRemove, cv=crossValidation, scoring='accuracy', n_jobs=-1)
-    #    DataHeatmap.append(tempRemove.mean())
-
-    temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring='precision_weighted', n_jobs=-1)
-    scores.append(temp.mean())
-    scores.append(temp.std())
-    temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring='recall_weighted', n_jobs=-1)
-    scores.append(temp.mean())
-    scores.append(temp.std())
-    temp = model_selection.cross_val_score(sclfStack, XData, yData, cv=crossValidation, scoring='accuracy', n_jobs=-1)
-    scores.append(temp.mean())
-    scores.append(temp.std())
-    temp = model_selection.cross_val_score(sclfStack, XData, yData, cv=crossValidation, scoring='precision_weighted', n_jobs=-1)
-    scores.append(temp.mean())
-    scores.append(temp.std())
-    temp = model_selection.cross_val_score(sclfStack, XData, yData, cv=crossValidation, scoring='recall_weighted', n_jobs=-1)
-    scores.append(temp.mean())
-    scores.append(temp.std())
 
     return 'Okay'
 
+def solve(sclf,sclfStack,XData,yData,crossValidation,scoringIn,loop):
+    scoresLoc = []
+    if (loop < 3):
+        temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
+    else:
+        temp = model_selection.cross_val_score(sclfStack, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
+    scoresLoc.append(temp.mean())
+    scoresLoc.append(temp.std())
+    return scoresLoc
+
+def processInput(indexValue,XData,yData,crossValidation,sclf):
+    XDataRemove = XData.copy()
+    XDataRemove.drop(indexValue, inplace=True)
+    yDataRemove = yData.copy()
+    del yDataRemove[indexValue]
+    tempRemove = model_selection.cross_val_score(sclf, XDataRemove, yDataRemove, cv=crossValidation, scoring='accuracy', n_jobs=-1)
+    return tempRemove.mean()
+
 # Sending the final results to be visualized as a line plot
 @app.route('/data/SendFinalResultsBacktoVisualize', methods=["GET", "POST"])
 def SendToPlotFinalResults():
@@ -2585,13 +2621,13 @@ def SendToPlotFinalResults():
     return jsonify(response)
 
 # Sending the final results to be visualized as a line plot
-@app.route('/data/SendInstancesImportance', methods=["GET", "POST"])
+#@app.route('/data/SendInstancesImportance', methods=["GET", "POST"])
-def SendImportInstances():
+#def SendImportInstances():
-    global DataHeatmap
+#    global DataHeatmap
-    response = {    
+#    response = {    
-        'instancesImportance': DataHeatmap
+#        'instancesImportance': DataHeatmap
-    }
+#    }
-    return jsonify(response)
+#    return jsonify(response)
 
 # Retrieve data from client 
 @cross_origin(origin='localhost',headers=['Content-Type','Authorization'])