remove

breast-cancer-wisconsin.csv

@@ -1,700 +0,0 @@
-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

cache_dir/joblib/run/EnsembleModel/6cfa19bb450bfb5dfa3fc39dbc8aa77e/metadata.json

@@ -1 +0,0 @@
-{"duration": 0.5628790855407715, "input_args": {"ClassifierIDsList": "''", "keyRetrieved": "0"}}

cache_dir/joblib/run/EnsembleModel/6cfa19bb450bfb5dfa3fc39dbc8aa77e/output.pkl

@@ -1 +0,0 @@
-<EFBFBD>N.

cache_dir/joblib/run/EnsembleModel/func_code.py

@@ -1,54 +0,0 @@
-# first line: 249
-def EnsembleModel (ClassifierIDsList, keyRetrieved): 
-
-    if (keyRetrieved == 0):
-        all_classifiers = []
-        all_classifiers.append(KNeighborsClassifier(n_neighbors=1))
-        all_classifiers.append(KNeighborsClassifier(n_neighbors=2))
-        all_classifiers.append(KNeighborsClassifier(n_neighbors=10))
-        all_classifiers.append(RandomForestClassifier(random_state=RANDOM_SEED, n_estimators = 1))
-        all_classifiers.append(RandomForestClassifier(random_state=RANDOM_SEED, n_estimators = 50))
-        lr = LogisticRegression()
-
-        sclf = StackingCVClassifier(classifiers=all_classifiers,
-                            use_probas=True,
-                            meta_classifier=lr,
-                            random_state=RANDOM_SEED,
-                            n_jobs = -1)
-        for clf, label in zip([sclf], 
-                        ['StackingClassifierAllClassifiers']):
-
-            scores = model_selection.cross_val_score(clf, XData, yData, 
-                                                    cv=5, scoring='accuracy')
-            print("Accuracy: %0.2f (+/- %0.2f) [%s]" 
-                % (scores.mean(), scores.std(), label))
-    else:        
-        all_classifiers = []                 
-        ClassifierIDsList = ClassifierIDsList.split('"')
-        for loop in ClassifierIDsList:
-            if ('ClassifierID' in loop):
-                if (loop == 'ClassifierID: 1'):
-                    all_classifiers.append(KNeighborsClassifier(n_neighbors=1))
-                elif (loop == 'ClassifierID: 2'):
-                    all_classifiers.append(KNeighborsClassifier(n_neighbors=2))
-                elif (loop == 'ClassifierID: 3'):
-                    all_classifiers.append(KNeighborsClassifier(n_neighbors=10))
-                elif (loop == 'ClassifierID: 4'):
-                    all_classifiers.append(RandomForestClassifier(random_state=RANDOM_SEED, n_estimators = 1))
-                else:
-                    all_classifiers.append(RandomForestClassifier(random_state=RANDOM_SEED, n_estimators = 50))
-
-        lr = LogisticRegression()
-
-        sclf = StackingCVClassifier(classifiers=all_classifiers,
-                            use_probas=True,
-                            meta_classifier=lr,
-                            random_state=RANDOM_SEED,
-                            n_jobs = -1)
-        for clf, label in zip([sclf], 
-                        ['StackingClassifierSelectedClassifiers']):
-
-            scores = model_selection.cross_val_score(clf, XData, yData, 
-                                                    cv=5, scoring='accuracy')
-            print("Accuracy: %0.2f (+/- %0.2f) [%s]" 
-                % (scores.mean(), scores.std(), label))

cache_dir/joblib/run/GridSearch/4b9f779431c047e8a87f8756bac85d64/metadata.json

@@ -1 +0,0 @@
-{"duration": 0.26462531089782715, "input_args": {"clf": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n                     weights='uniform')", "params": "{'n_neighbors': [1, 2, 10]}", "scoring": "{'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}", "FI": "0"}}

cache_dir/joblib/run/GridSearch/a32924a20c3a8483b7f841d3c9c7508f/metadata.json

@@ -1 +0,0 @@
-{"duration": 0.792708158493042, "input_args": {"clf": "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n                       max_depth=None, max_features='auto', max_leaf_nodes=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='warn',\n                       n_jobs=None, oob_score=False, random_state=None,\n                       verbose=0, warm_start=False)", "params": "{'n_estimators': [10, 50]}", "scoring": "{'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}", "FI": "1"}}

cache_dir/joblib/run/GridSearch/func_code.py

@@ -1,51 +0,0 @@
-# first line: 85
-def GridSearch(clf, params, scoring, FI):
-    
-    grid = GridSearchCV(estimator=clf, 
-                param_grid=params,
-                scoring=scoring, 
-                cv=5,
-                refit='accuracy',
-                n_jobs = -1)
-
-    grid.fit(XData, yData)
-
-    cv_results = []
-    cv_results.append(grid.cv_results_)
-    df_cv_results = pd.DataFrame.from_dict(cv_results)
-
-    number_of_classifiers = len(df_cv_results.iloc[0][0])
-    number_of_columns = len(df_cv_results.iloc[0])
-    df_cv_results_per_item = []
-    df_cv_results_per_row = []
-
-    for i in range(number_of_classifiers):
-        df_cv_results_per_item = []
-        for column in df_cv_results.iloc[0]:
-            df_cv_results_per_item.append(column[i])
-        df_cv_results_per_row.append(df_cv_results_per_item)
-
-    df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns)
-    parameters = df_cv_results_classifiers['params']
-    FeatureImp = []
-    target_names = ['class 0', 'class 1', 'class 2']
-    for eachClassifierParams in grid.cv_results_['params']:
-        eachClassifierParamsDictList = {}
-        for key, value in eachClassifierParams.items():
-            Listvalue = []
-            Listvalue.append(value)
-            eachClassifierParamsDictList[key] = Listvalue
-        grid = GridSearchCV(estimator=clf, 
-            param_grid=eachClassifierParamsDictList,
-            scoring=scoring, 
-            cv=5,
-            refit='accuracy',
-            n_jobs = -1)
-        print(eachClassifierParamsDictList)   
-        grid.fit(XData, yData)
-        yPredict = grid.predict(XData)
-        print(classification_report(yData, yPredict, target_names=target_names))
-        if (FI == 1):
-            FeatureImp.append(grid.best_estimator_.feature_importances_)
-
-    return df_cv_results_classifiers, parameters, FeatureImp

cache_dir/joblib/run/GridSearchForModels/6ca078b89780ce51f8c908be394b0a91/metadata.json

@@ -1 +0,0 @@
-{"duration": 13.885742902755737, "input_args": {"clf": "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n                       max_depth=None, max_features='auto', max_leaf_nodes=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='warn',\n                       n_jobs=None, oob_score=False, random_state=None,\n                       verbose=0, warm_start=False)", "params": "{'n_estimators': [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], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "factors": "[1, 1, 1, 1, 1, 1]"}}

cache_dir/joblib/run/GridSearchForModels/b32ac658efc96fe80763fbc6cdd8559c/metadata.json

@@ -1 +0,0 @@
-{"duration": 36.23512291908264, "input_args": {"clf": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n                     weights='uniform')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']}", "eachAlgor": "'KNN'", "factors": "[1, 1, 1, 1, 1, 1]"}}

cache_dir/joblib/run/GridSearchForModels/func_code.py

@@ -1,96 +0,0 @@
-# first line: 714
-def GridSearchForModels(clf, params, eachAlgor, factors):
-
-    # scoring parameters
-    global scoring 
-
-    # number of scoring parameters
-    global NumberofscoringMetrics
-
-    # crossvalidation number
-    global crossValidation
-
-    # instantiate spark session
-    spark = (   
-        SparkSession    
-        .builder    
-        .getOrCreate()    
-        )
-    sc = spark.sparkContext 
-
-    # this is the grid we use to train the models
-    grid = DistGridSearchCV(    
-        estimator=clf, param_grid=params,     
-        sc=sc, cv=crossValidation, refit='accuracy', scoring=scoring,
-        verbose=0, n_jobs=-1)
-
-    # fit and extract the probabilities
-    grid.fit(XData, yData)
-    yPredict = grid.predict(XData)
-
-    # process the results
-    cv_results = []
-    cv_results.append(grid.cv_results_)
-    df_cv_results = pd.DataFrame.from_dict(cv_results)
-
-    # number of models stored
-    number_of_models = len(df_cv_results.iloc[0][0])
-
-    # initialize results per row
-    df_cv_results_per_row = []
-
-    # loop through number of models
-    for i in range(number_of_models):
-         # initialize results per item
-        df_cv_results_per_item = []
-        for column in df_cv_results.iloc[0]:
-            df_cv_results_per_item.append(column[i])
-        df_cv_results_per_row.append(df_cv_results_per_item)
-
-    # store the results into a pandas dataframe
-    df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns)
-
-    # copy and filter in order to get only the metrics
-    metrics = df_cv_results_classifiers.copy()
-    metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard']) 
-
-    # control the factors
-    sumperModel = []
-    for row in metrics.iterrows():
-        rowSum = 0
-        lengthFactors = NumberofscoringMetrics
-        for loop,elements in enumerate(row):
-            lengthFactors = lengthFactors -  1 + factors[loop]
-            rowSum = elements*factors[loop] + rowSum
-        if lengthFactors is 0:
-            sumperModel = 0
-        else:
-            sumperModel.append(rowSum/lengthFactors)
-    # summarize all models metrics
-    summarizedMetrics = pd.DataFrame(sumperModel)
-    summarizedMetrics.rename(columns={0:'sum'})
-    
-    yPredictProb.append(grid.predict_proba(XData))
-    
-    # retrieve target names (class names)
-    global target_names
-    PerClassMetric = []
-    PerClassMetric.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
-    PerClassMetricPandas = pd.DataFrame(PerClassMetric)  
-    print(PerClassMetricPandas)
-    del PerClassMetricPandas['accuracy']  
-    del PerClassMetricPandas['macro avg']  
-    del PerClassMetricPandas['weighted avg']  
-    PerClassMetricPandas = PerClassMetricPandas.to_json()
-
-    # concat parameters and performance
-    parameters = pd.DataFrame(df_cv_results_classifiers['params'])
-    parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
-    parametersPerformancePerModel = parametersPerformancePerModel.to_json()
-    
-    # make global the parameters performance to send it back
-    global allParametersPerformancePerModel
-    allParametersPerformancePerModel.append(parametersPerformancePerModel)
-    allParametersPerformancePerModel.append(PerClassMetricPandas)
-
-    return 'Everything is okay'

cache_dir/joblib/run/GridSearchForParameters/6ca078b89780ce51f8c908be394b0a91/metadata.json

@@ -1 +0,0 @@
-{"duration": 11.80344295501709, "input_args": {"clf": "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n                       max_depth=None, max_features='auto', max_leaf_nodes=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='warn',\n                       n_jobs=None, oob_score=False, random_state=None,\n                       verbose=0, warm_start=False)", "params": "{'n_estimators': [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], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "factors": "[1, 1, 1, 1, 1, 1]"}}

cache_dir/joblib/run/GridSearchForParameters/b32ac658efc96fe80763fbc6cdd8559c/metadata.json

@@ -1 +0,0 @@
-{"duration": 28.940529108047485, "input_args": {"clf": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n                     weights='uniform')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']}", "eachAlgor": "'KNN'", "factors": "[1, 1, 1, 1, 1, 1]"}}

cache_dir/joblib/run/GridSearchForParameters/func_code.py

@@ -1,76 +0,0 @@
-# first line: 705
-def GridSearchForParameters(clf, params, eachAlgor, factors):
-
-    global scoring 
-    global NumberofscoringMetrics
-
-    # instantiate spark session
-    spark = (   
-        SparkSession    
-        .builder    
-        .getOrCreate()    
-        )
-    sc = spark.sparkContext 
-
-    scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}
-    global crossValidation
-    NumberofscoringMetrics = len(scoring)
-
-    grid = DistGridSearchCV(    
-        estimator=clf, param_grid=params,     
-        sc=sc, cv=crossValidation, refit='accuracy', scoring=scoring,
-        verbose=0, n_jobs=-1)
-
-    grid.fit(XData, yData)
-    yPredict = grid.predict(XData)
-    cv_results = []
-    cv_results.append(grid.cv_results_)
-    df_cv_results = pd.DataFrame.from_dict(cv_results)
-    number_of_classifiers = len(df_cv_results.iloc[0][0])
-
-    df_cv_results_per_item = []
-    df_cv_results_per_row = []
-
-    for i in range(number_of_classifiers):
-        df_cv_results_per_item = []
-        for column in df_cv_results.iloc[0]:
-            df_cv_results_per_item.append(column[i])
-        df_cv_results_per_row.append(df_cv_results_per_item)
-    df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns)
-
-    global allParametersPerformancePerModel
-    global parametersPerformancePerModel
-
-    metrics = df_cv_results_classifiers.copy()
-    metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard'])
-    sumperModel = []
-    global rowSum
-    for index, row in metrics.iterrows():
-        rowSum = 0
-        lengthFactors = NumberofscoringMetrics
-        for loop,elements in enumerate(row):
-            lengthFactors = lengthFactors -  1 + factors[loop]
-            rowSum = elements*factors[loop] + rowSum
-        if lengthFactors is 0:
-            sumperModel = 0
-        else:
-            sumperModel.append(rowSum/lengthFactors)
-    global target_names
-    global PerClassMetric
-    global PerClassMetricPandas
-    PerClassMetric = []
-    yPredictProb.append(grid.predict_proba(XData))
-    PerClassMetric.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
-    PerClassMetricPandas = pd.DataFrame(PerClassMetric)  
-    del PerClassMetricPandas['accuracy']  
-    del PerClassMetricPandas['macro avg']  
-    del PerClassMetricPandas['weighted avg']  
-    summarizedMetrics = pd.DataFrame(sumperModel)
-    summarizedMetrics.rename(columns={0:'sum'})
-    parameters = pd.DataFrame(df_cv_results_classifiers['params'])
-    parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
-    PerClassMetricPandas = PerClassMetricPandas.to_json()
-    parametersPerformancePerModel = parametersPerformancePerModel.to_json()
-    allParametersPerformancePerModel.append(parametersPerformancePerModel)
-    allParametersPerformancePerModel.append(PerClassMetricPandas)
-    return 'Everything is okay'

cache_dir/joblib/run/InitializeEnsemble/72a755383fba437e4dead6ff3e3d81e3/metadata.json

@@ -1 +0,0 @@
-{"duration": 0.5897552967071533, "input_args": {}}

cache_dir/joblib/run/InitializeEnsemble/func_code.py

@@ -1,105 +0,0 @@
-# first line: 133
-def InitializeEnsemble():  
-    DataResults = copy.deepcopy(DataResultsRaw)
-    for dictionary in DataResultsRaw:
-        for key in dictionary.keys():
-            if (key.find('*') != -1):
-                target = key
-                continue
-        continue
-
-    DataResultsRaw.sort(key=lambda x: x[target], reverse=True)
-    DataResults.sort(key=lambda x: x[target], reverse=True)
-
-    for dictionary in DataResults:
-        del dictionary['_id']
-        del dictionary['InstanceID']
-        del dictionary[target]
-
-    AllTargets = [o[target] for o in DataResultsRaw]
-    AllTargetsFloatValues = []
-    previous = None
-    Class = 0
-    for i, value in enumerate(AllTargets):
-        if (i == 0):
-            previous = value
-        if (value == previous):
-            AllTargetsFloatValues.append(Class)
-        else:
-            Class = Class + 1
-            AllTargetsFloatValues.append(Class)
-            previous = value
-
-    ArrayDataResults = pd.DataFrame.from_dict(DataResults)
-
-    global XData, yData, RANDOM_SEED
-    XData, yData = ArrayDataResults, AllTargetsFloatValues
-    warnings.simplefilter('ignore')
-
-    RANDOM_SEED = 42
-
-    ClassifierIDsList = ''
-    key = 0
-
-    # Initializing models
-
-    #scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted', 'neg_log_loss': 'neg_log_loss', 'r2': 'r2', 'neg_mean_absolute_error': 'neg_mean_absolute_error', 'neg_mean_absolute_error': 'neg_mean_absolute_error'}
-    scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}
-    NumberofscoringMetrics = len(scoring)
-    results = []
-
-    clf = KNeighborsClassifier()
-    params = {'n_neighbors': [1, 2, 10]}
-    IF = 0
-    #params = {'n_neighbors': [1, 3, 5],
-    #        'weights': ['uniform', 'distance'],
-    #        'metric': ['euclidean', 'manhattan']}
-    
-    results.append(GridSearch(clf, params, scoring, IF))
-
-    clf = RandomForestClassifier()
-    params = {'n_estimators': [10, 50]}
-    IF = 1
-
-    results.append(GridSearch(clf, params, scoring, IF))
-
-    df_cv_results_classifiers = pd.concat([results[0][0], results[1][0]], ignore_index=True, sort=False)
-    parameters = pd.concat([results[0][1], results[1][1]], ignore_index=True, sort=False)
-
-    classifiersIDPlusParams = [] 
-    classifierID = 0
-    for oneClassifier in parameters: 
-        classifierID = classifierID + 1
-        classifiersIDPlusParams.append(classifierID)
-        classifiersIDPlusParams.append(oneClassifier)
-
-    del df_cv_results_classifiers['params']
-    df_cv_results_classifiers_metrics = df_cv_results_classifiers.copy()
-
-
-    df_cv_results_classifiers_metrics = df_cv_results_classifiers_metrics.ix[:, 0:NumberofscoringMetrics+1]
-    del df_cv_results_classifiers_metrics['mean_fit_time']
-    del df_cv_results_classifiers_metrics['mean_score_time']
-
-    sumPerClassifier = []
-    for index, row in df_cv_results_classifiers_metrics.iterrows():
-        rowSum = 0
-        for elements in row:
-            rowSum = elements + rowSum
-        sumPerClassifier.append(rowSum)
-        
-    XClassifiers = df_cv_results_classifiers_metrics
-    embedding = MDS(n_components=2, random_state=RANDOM_SEED)
-    X_transformed = embedding.fit_transform(XClassifiers).T
-    
-    X_transformed = X_transformed.tolist()
-
-    EnsembleModel(ClassifierIDsList, key)
-
-    global ResultsforOverview
-    ResultsforOverview = []
-    ResultsforOverview.append(json.dumps(sumPerClassifier)) 
-    ResultsforOverview.append(json.dumps(X_transformed)) 
-    ResultsforOverview.append(json.dumps(classifiersIDPlusParams)) 
-
-    return ResultsforOverview