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 `Expression` objects. These expressions are compiled to SQL scalar expressions and included in the generated `CREATE MODEL` DDL statement.

- Added `bigframes.core.expression.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".

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

Author: google-labs-jules[bot]

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

@@ -25,6 +25,7 @@
 import bigframes.dataframe as dataframe
 import bigframes.ml.base
 import bigframes.session
+import bigframes.core.expression as ex
 from bigframes.bigquery._operations import utils
 
 
@@ -50,7 +51,9 @@ def create_model(
     input_schema: Optional[Mapping[str, str]] = None,
     output_schema: Optional[Mapping[str, str]] = None,
     connection_name: Optional[str] = None,
-    options: Optional[Mapping[str, Union[str, int, float, bool, list]]] = None,
+    options: Optional[
+        Mapping[str, Union[str, int, float, bool, list, "ex.Expression"]]
+    ] = None,
     training_data: Optional[Union[pd.DataFrame, dataframe.DataFrame, str]] = None,
     custom_holiday: Optional[Union[pd.DataFrame, dataframe.DataFrame, str]] = None,
     session: Optional[bigframes.session.Session] = None,
@@ -78,7 +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]], optional):
+        options (Mapping[str, Union[str, int, float, bool, list, bigframes.core.expression.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 +16,9 @@
 
 from typing import Any, Dict, List, Mapping, Optional, Union
 
+import bigframes.core.expression as ex
 from bigframes.core.compile.sqlglot import sql as sg_sql
+from bigframes.core.compile.sqlglot.expression_compiler import expression_compiler
 
 
 def create_model_ddl(
@@ -28,7 +30,9 @@ def create_model_ddl(
     input_schema: Optional[Mapping[str, str]] = None,
     output_schema: Optional[Mapping[str, str]] = None,
     connection_name: Optional[str] = None,
-    options: Optional[Mapping[str, Union[str, int, float, bool, list]]] = None,
+    options: Optional[
+        Mapping[str, Union[str, int, float, bool, list, "ex.Expression"]]
+    ] = None,
     training_data: Optional[str] = None,
     custom_holiday: Optional[str] = None,
 ) -> str:
@@ -70,7 +74,10 @@ def create_model_ddl(
     if options:
         rendered_options = []
         for option_name, option_value in options.items():
-            if isinstance(option_value, (list, tuple)):
+            if isinstance(option_value, ex.Expression):
+                sg_expr = expression_compiler.compile_expression(option_value)
+                rendered_val = sg_sql.to_sql(sg_expr)
+            elif isinstance(option_value, (list, tuple)):
                 # Handle list options like model_registry="vertex_ai"
                 # wait, usually options are key=value.
                 # if value is list, it is [val1, val2]

packages/bigframes/tests/unit/core/sql/snapshots/test_ml/test_create_model_expression_option/create_model_expression_option.sql

@@ -0,0 +1,3 @@
+CREATE MODEL `my_model`
+OPTIONS(l2_reg = 0.1, booster_type = 'gbtree')
+AS SELECT * FROM t

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

@@ -97,6 +97,20 @@ def test_create_model_list_option(snapshot):
     snapshot.assert_match(sql, "create_model_list_option.sql")
 
 
+def test_create_model_expression_option(snapshot):
+    import bigframes.core.expression as ex
+
+    sql = bigframes.core.sql.ml.create_model_ddl(
+        model_name="my_model",
+        options={
+            "l2_reg": ex.ScalarConstantExpression(0.1, None),
+            "booster_type": "gbtree",
+        },
+        training_data="SELECT * FROM t",
+    )
+    snapshot.assert_match(sql, "create_model_expression_option.sql")
+
+
 def test_evaluate_model_basic(snapshot):
     sql = bigframes.core.sql.ml.evaluate(
         model_name="my_project.my_dataset.my_model",