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875 | 875 | "fold_index_variable.name = \"FoldIndex\"\n", |
876 | 876 | "fold_index_variable.type = \"Numerical\"\n", |
877 | 877 | "fold_index_variable.used = False\n", |
878 | | - "fold_index_variable.rule = \"Ceil(Product(\" + str(fold_number) + \", Random()))\"\n", |
879 | 878 | "dictionary.add_variable(fold_index_variable)\n", |
880 | 879 | "\n", |
| 880 | + "# Create fold indexing rule and set it on `fold_index_variable`\n", |
| 881 | + "dictionary.set_variable_rule(\n", |
| 882 | + " fold_index_variable.name,\n", |
| 883 | + " kh.Rule(\"Ceil\", kh.Rule(\"Product\", fold_number, kh.Rule(\"Random()\"))),\n", |
| 884 | + ")\n", |
| 885 | + "\n", |
881 | 886 | "# Add variables that indicate if the instance is in the train dataset:\n", |
882 | 887 | "for fold_index in range(1, fold_number + 1):\n", |
883 | 888 | " is_in_train_dataset_variable = kh.Variable()\n", |
884 | 889 | " is_in_train_dataset_variable.name = \"IsInTrainDataset\" + str(fold_index)\n", |
885 | 890 | " is_in_train_dataset_variable.type = \"Numerical\"\n", |
886 | 891 | " is_in_train_dataset_variable.used = False\n", |
887 | | - " is_in_train_dataset_variable.rule = \"NEQ(FoldIndex, \" + str(fold_index) + \")\"\n", |
888 | 892 | " dictionary.add_variable(is_in_train_dataset_variable)\n", |
| 893 | + " dictionary.set_variable_rule(\n", |
| 894 | + " is_in_train_dataset_variable.name,\n", |
| 895 | + " kh.Rule(\"NEQ\", fold_index_variable, fold_index),\n", |
| 896 | + " )\n", |
889 | 897 | "\n", |
890 | 898 | "# Print dictionary with fold variables\n", |
891 | 899 | "print(\"Dictionary file with fold variables\")\n", |
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