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788 | 788 | "fold_index_variable.name = \"FoldIndex\"\n", |
789 | 789 | "fold_index_variable.type = \"Numerical\"\n", |
790 | 790 | "fold_index_variable.used = False\n", |
791 | | - "fold_index_variable.rule = \"Ceil(Product(\" + str(fold_number) + \", Random()))\"\n", |
792 | 791 | "dictionary.add_variable(fold_index_variable)\n", |
793 | 792 | "\n", |
| 793 | + "# Create fold indexing rule and set it on `fold_index_variable`\n", |
| 794 | + "dictionary.set_variable_rule(\n", |
| 795 | + " fold_index_variable.name,\n", |
| 796 | + " kh.Rule(\"Ceil\", kh.Rule(\"Product\", fold_number, kh.Rule(\"Random()\"))),\n", |
| 797 | + ")\n", |
| 798 | + "\n", |
794 | 799 | "# Add variables that indicate if the instance is in the train dataset:\n", |
795 | 800 | "for fold_index in range(1, fold_number + 1):\n", |
796 | 801 | " is_in_train_dataset_variable = kh.Variable()\n", |
797 | 802 | " is_in_train_dataset_variable.name = \"IsInTrainDataset\" + str(fold_index)\n", |
798 | 803 | " is_in_train_dataset_variable.type = \"Numerical\"\n", |
799 | 804 | " is_in_train_dataset_variable.used = False\n", |
800 | | - " is_in_train_dataset_variable.rule = \"NEQ(FoldIndex, \" + str(fold_index) + \")\"\n", |
801 | 805 | " dictionary.add_variable(is_in_train_dataset_variable)\n", |
| 806 | + " dictionary.set_variable_rule(\n", |
| 807 | + " is_in_train_dataset_variable.name,\n", |
| 808 | + " kh.Rule(\"NEQ\", fold_index_variable, fold_index),\n", |
| 809 | + " )\n", |
802 | 810 | "\n", |
803 | 811 | "# Print dictionary with fold variables\n", |
804 | 812 | "print(\"Dictionary file with fold variables\")\n", |
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