SIENTIAPDE-1255: Add tests to ensure get_required_columns removes columns from self_operations and cross_operations.

tests/sientia/test_models.py

@@ -6,7 +6,23 @@ import numpy as np
 import pandas as pd
 from pytest import raises
 
-from model_manager.sientia.models import DataPreprocessor, LinearRegressionModel
+from model_manager.sientia.models import (
+    DataPreprocessor,
+    LinearRegressionModel,
+)
+
+
+class _IterableWithContains:
+    def __init__(self, iterable, contains_values):
+        self._iterable = iterable
+        self._contains = set(contains_values)
+
+    def __iter__(self):
+        return iter(self._iterable)
+
+    def __contains__(self, item):
+        return item in self._contains
+
 
 # LinearRegressionModel Tests
 
@@ -573,6 +589,31 @@ def test_data_preprocessor_get_required_columns_removes_duplicates():
     assert 'var1' not in result or result.count('var1') <= 1
 
 
+def test_data_preprocessor_get_required_columns_removes_self_operations_branch():
+    """Ensure line 278 removes columns present in self_operations iterable."""
+    preprocessor = DataPreprocessor(
+        self_operations=_IterableWithContains(['{var1}_{pow}_{2}'], contains_values=['var1'])
+    )
+    existing_columns: list[str] = []
+
+    result = preprocessor.get_required_columns(existing_columns)
+
+    assert 'var1' not in result
+
+
+def test_data_preprocessor_get_required_columns_removes_cross_operations_branch():
+    """Ensure line 285 removes columns present in cross_operations iterable."""
+    preprocessor = DataPreprocessor(
+        cross_operations=_IterableWithContains(['{var1}_{*}_{var2}'], contains_values=['var1'])
+    )
+    existing_columns: list[str] = []
+
+    result = preprocessor.get_required_columns(existing_columns)
+
+    assert 'var1' not in result
+    assert 'var2' in result
+
+
 def test_data_preprocessor_get_required_columns_removes_created_lags():
     """Test get_required_columns removes columns from created_lags when column is in created_lags dict - covers line 292."""
     preprocessor = DataPreprocessor(created_lags={'var1': 1, 'var2': 1})