feat(bigframes): Support Expression objects in create_model options

This change allows the `options` parameter of `bigframes.bigquery._operations.ml.create_model` to accept BigFrames `col.Expression` objects. These expressions are compiled to SQL scalar expressions and included in the generated `CREATE MODEL` DDL statement.

- Added `bigframes.core.col.Expression` type support in the `options` dict.
- Updated `create_model_ddl` to handle compiling expressions using `expression_compiler`.
- Added `test_create_model_expression_option` snapshot test to verify the generated "golden SQL", using an expression that calls a function on a literal value (e.g. 0.1 * 10).
- Moved test imports to the top level to adhere to PEP 8 and ran `ruff format`.

Co-authored-by: tswast <247555+tswast@users.noreply.github.com>

Author: google-labs-jules[bot]

packages/bigframes/bigframes/bigquery/_operations/ml.py

@@ -25,7 +25,7 @@
 import bigframes.dataframe as dataframe
 import bigframes.ml.base
 import bigframes.session
-import bigframes.core.expression as ex
+import bigframes.core.col as col
 from bigframes.bigquery._operations import utils
 
 
@@ -52,7 +52,7 @@ def create_model(
     output_schema: Optional[Mapping[str, str]] = None,
     connection_name: Optional[str] = None,
     options: Optional[
-        Mapping[str, Union[str, int, float, bool, list, "ex.Expression"]]
+        Mapping[str, Union[str, int, float, bool, list, "col.Expression"]]
     ] = None,
     training_data: Optional[Union[pd.DataFrame, dataframe.DataFrame, str]] = None,
     custom_holiday: Optional[Union[pd.DataFrame, dataframe.DataFrame, str]] = None,
@@ -81,7 +81,7 @@ def create_model(
             The OUTPUT clause, which specifies the schema of the output data.
         connection_name (str, optional):
             The connection to use for the model.
-        options (Mapping[str, Union[str, int, float, bool, list, bigframes.core.expression.Expression]], optional):
+        options (Mapping[str, Union[str, int, float, bool, list, bigframes.core.col.Expression]], optional):
             The OPTIONS clause, which specifies the model options.
         training_data (Union[bigframes.pandas.DataFrame, str], optional):
             The query or DataFrame to use for training the model.

packages/bigframes/bigframes/core/sql/ml.py

@@ -16,7 +16,7 @@
 
 from typing import Any, Dict, List, Mapping, Optional, Union
 
-import bigframes.core.expression as ex
+import bigframes.core.col as col
 from bigframes.core.compile.sqlglot import sql as sg_sql
 from bigframes.core.compile.sqlglot.expression_compiler import expression_compiler
 
@@ -31,7 +31,7 @@ def create_model_ddl(
     output_schema: Optional[Mapping[str, str]] = None,
     connection_name: Optional[str] = None,
     options: Optional[
-        Mapping[str, Union[str, int, float, bool, list, "ex.Expression"]]
+        Mapping[str, Union[str, int, float, bool, list, "col.Expression"]]
     ] = None,
     training_data: Optional[str] = None,
     custom_holiday: Optional[str] = None,
@@ -74,8 +74,8 @@ def create_model_ddl(
     if options:
         rendered_options = []
         for option_name, option_value in options.items():
-            if isinstance(option_value, ex.Expression):
-                sg_expr = expression_compiler.compile_expression(option_value)
+            if isinstance(option_value, col.Expression):
+                sg_expr = expression_compiler.compile_expression(option_value._value)
                 rendered_val = sg_sql.to_sql(sg_expr)
             elif isinstance(option_value, (list, tuple)):
                 # Handle list options like model_registry="vertex_ai"

packages/bigframes/tests/unit/core/sql/test_ml.py

@@ -14,6 +14,7 @@
 
 import pytest
 
+import bigframes.core.col as col
 import bigframes.core.expression as ex
 import bigframes.core.sql.ml
 import bigframes.dtypes as dtypes
@@ -105,8 +106,8 @@ def test_create_model_expression_option(snapshot):
     # e.g. 0.1 * 10
     literal_expr = ex.ScalarConstantExpression(0.1, dtypes.FLOAT_DTYPE)
     multiplier_expr = ex.ScalarConstantExpression(10, dtypes.INT_DTYPE)
-    math_expr = ex.OpExpression(
-        op=numeric_ops.mul_op, inputs=(literal_expr, multiplier_expr)
+    math_expr = col.Expression(
+        ex.OpExpression(op=numeric_ops.mul_op, inputs=(literal_expr, multiplier_expr))
     )
 
     sql = bigframes.core.sql.ml.create_model_ddl(