Update relevant Core sample according to the new Rule API support

Author: popescu-v

doc/samples/samples.rst

@@ -581,17 +581,21 @@ Samples
     fold_index_variable.name = "FoldIndex"
     fold_index_variable.type = "Numerical"
     fold_index_variable.used = False
-    fold_index_variable.rule = "Ceil(Product(" + str(fold_number) + ",  Random()))"
     dictionary.add_variable(fold_index_variable)
 
+    # Create fold indexing rule and set it on `fold_index_variable`
+    fold_index_rule = kh.Rule("Ceil", kh.Rule("Product", fold_number, kh.Rule("Random()")))
+    dictionary.set_variable_rule(fold_index_variable.name, fold_index_rule)
+
     # Add variables that indicate if the instance is in the train dataset:
     for fold_index in range(1, fold_number + 1):
         is_in_train_dataset_variable = kh.Variable()
         is_in_train_dataset_variable.name = "IsInTrainDataset" + str(fold_index)
         is_in_train_dataset_variable.type = "Numerical"
         is_in_train_dataset_variable.used = False
-        is_in_train_dataset_variable.rule = "NEQ(FoldIndex, " + str(fold_index) + ")"
         dictionary.add_variable(is_in_train_dataset_variable)
+        indicator_rule = kh.Rule("NEQ", fold_index_variable, fold_index)
+        dictionary.set_variable_rule(is_in_train_dataset_variable.name, indicator_rule)
 
     # Print dictionary with fold variables
     print("Dictionary file with fold variables")

khiops/samples/samples.ipynb

@@ -788,17 +788,21 @@
     "fold_index_variable.name = \"FoldIndex\"\n",
     "fold_index_variable.type = \"Numerical\"\n",
     "fold_index_variable.used = False\n",
-    "fold_index_variable.rule = \"Ceil(Product(\" + str(fold_number) + \",  Random()))\"\n",
     "dictionary.add_variable(fold_index_variable)\n",
     "\n",
+    "# Create fold indexing rule and set it on `fold_index_variable`\n",
+    "fold_index_rule = kh.Rule(\"Ceil\", kh.Rule(\"Product\", fold_number, kh.Rule(\"Random()\")))\n",
+    "dictionary.set_variable_rule(fold_index_variable.name, fold_index_rule)\n",
+    "\n",
     "# Add variables that indicate if the instance is in the train dataset:\n",
     "for fold_index in range(1, fold_number + 1):\n",
     "    is_in_train_dataset_variable = kh.Variable()\n",
     "    is_in_train_dataset_variable.name = \"IsInTrainDataset\" + str(fold_index)\n",
     "    is_in_train_dataset_variable.type = \"Numerical\"\n",
     "    is_in_train_dataset_variable.used = False\n",
-    "    is_in_train_dataset_variable.rule = \"NEQ(FoldIndex, \" + str(fold_index) + \")\"\n",
     "    dictionary.add_variable(is_in_train_dataset_variable)\n",
+    "    indicator_rule = kh.Rule(\"NEQ\", fold_index_variable, fold_index)\n",
+    "    dictionary.set_variable_rule(is_in_train_dataset_variable.name, indicator_rule)\n",
     "\n",
     "# Print dictionary with fold variables\n",
     "print(\"Dictionary file with fold variables\")\n",

khiops/samples/samples.py

@@ -649,17 +649,23 @@ def train_predictor_with_cross_validation():
     fold_index_variable.name = "FoldIndex"
     fold_index_variable.type = "Numerical"
     fold_index_variable.used = False
-    fold_index_variable.rule = "Ceil(Product(" + str(fold_number) + ",  Random()))"
     dictionary.add_variable(fold_index_variable)
 
+    # Create fold indexing rule and set it on `fold_index_variable`
+    fold_index_rule = kh.Rule(
+        "Ceil", kh.Rule("Product", fold_number, kh.Rule("Random()"))
+    )
+    dictionary.set_variable_rule(fold_index_variable.name, fold_index_rule)
+
     # Add variables that indicate if the instance is in the train dataset:
     for fold_index in range(1, fold_number + 1):
         is_in_train_dataset_variable = kh.Variable()
         is_in_train_dataset_variable.name = "IsInTrainDataset" + str(fold_index)
         is_in_train_dataset_variable.type = "Numerical"
         is_in_train_dataset_variable.used = False
-        is_in_train_dataset_variable.rule = "NEQ(FoldIndex, " + str(fold_index) + ")"
         dictionary.add_variable(is_in_train_dataset_variable)
+        indicator_rule = kh.Rule("NEQ", fold_index_variable, fold_index)
+        dictionary.set_variable_rule(is_in_train_dataset_variable.name, indicator_rule)
 
     # Print dictionary with fold variables
     print("Dictionary file with fold variables")