docs: fix cast method shown on public docs (#2436)

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Fixes #<issue_number_goes_here> 🦕

Author: GarrettWu

bigframes/ml/base.py

@@ -24,7 +24,8 @@
 """
 
 import abc
-from typing import cast, Optional, TypeVar, Union
+import typing
+from typing import Optional, TypeVar, Union
 import warnings
 
 import bigframes_vendored.sklearn.base
@@ -133,7 +134,7 @@ def register(self: _T, vertex_ai_model_id: Optional[str] = None) -> _T:
                 self._bqml_model = self._create_bqml_model()  # type: ignore
             except AttributeError:
                 raise RuntimeError("A model must be trained before register.")
-        self._bqml_model = cast(core.BqmlModel, self._bqml_model)
+        self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
 
         self._bqml_model.register(vertex_ai_model_id)
         return self
@@ -286,7 +287,7 @@ def _predict_and_retry(
                 bpd.concat([df_result, df_succ]) if df_result is not None else df_succ
             )
 
-        df_result = cast(
+        df_result = typing.cast(
             bpd.DataFrame,
             bpd.concat([df_result, df_fail]) if df_result is not None else df_fail,
         )
@@ -306,7 +307,7 @@ def _extract_output_names(self):
 
         output_names = []
         for transform_col in self._bqml_model._model._properties["transformColumns"]:
-            transform_col_dict = cast(dict, transform_col)
+            transform_col_dict = typing.cast(dict, transform_col)
             # pass the columns that are not transformed
             if "transformSql" not in transform_col_dict:
                 continue

bigframes/ml/compose.py

@@ -21,7 +21,7 @@
 import re
 import types
 import typing
-from typing import cast, Iterable, List, Optional, Set, Tuple, Union
+from typing import Iterable, List, Optional, Set, Tuple, Union
 
 from bigframes_vendored import constants
 import bigframes_vendored.sklearn.compose._column_transformer
@@ -218,7 +218,7 @@ def camel_to_snake(name):
 
         output_names = []
         for transform_col in bq_model._properties["transformColumns"]:
-            transform_col_dict = cast(dict, transform_col)
+            transform_col_dict = typing.cast(dict, transform_col)
             # pass the columns that are not transformed
             if "transformSql" not in transform_col_dict:
                 continue
@@ -282,7 +282,7 @@ def _merge(
             return self  # SQLScalarColumnTransformer only work inside ColumnTransformer
         feature_columns_sorted = sorted(
             [
-                cast(str, feature_column.name)
+                typing.cast(str, feature_column.name)
                 for feature_column in bq_model.feature_columns
             ]
         )

bigframes/ml/core.py

@@ -18,7 +18,8 @@
 
 import dataclasses
 import datetime
-from typing import Callable, cast, Iterable, Mapping, Optional, Union
+import typing
+from typing import Callable, Iterable, Mapping, Optional, Union
 import uuid
 
 from google.cloud import bigquery
@@ -376,7 +377,7 @@ def copy(self, new_model_name: str, replace: bool = False) -> BqmlModel:
     def register(self, vertex_ai_model_id: Optional[str] = None) -> BqmlModel:
         if vertex_ai_model_id is None:
             # vertex id needs to start with letters. https://cloud.google.com/vertex-ai/docs/general/resource-naming
-            vertex_ai_model_id = "bigframes_" + cast(str, self._model.model_id)
+            vertex_ai_model_id = "bigframes_" + typing.cast(str, self._model.model_id)
 
         # truncate as Vertex ID only accepts 63 characters, easily exceeding the limit for temp models.
         # The possibility of conflicts should be low.

bigframes/ml/imported.py

@@ -16,7 +16,8 @@
 
 from __future__ import annotations
 
-from typing import cast, Mapping, Optional
+import typing
+from typing import Mapping, Optional
 
 from google.cloud import bigquery
 
@@ -78,7 +79,7 @@ def predict(self, X: utils.ArrayType) -> bpd.DataFrame:
             if self.model_path is None:
                 raise ValueError("Model GCS path must be provided.")
             self._bqml_model = self._create_bqml_model()
-        self._bqml_model = cast(core.BqmlModel, self._bqml_model)
+        self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
 
         (X,) = utils.batch_convert_to_dataframe(X)
 
@@ -99,7 +100,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> TensorFlowModel:
             if self.model_path is None:
                 raise ValueError("Model GCS path must be provided.")
             self._bqml_model = self._create_bqml_model()
-        self._bqml_model = cast(core.BqmlModel, self._bqml_model)
+        self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
 
         new_model = self._bqml_model.copy(model_name, replace)
         return new_model.session.read_gbq_model(model_name)
@@ -157,7 +158,7 @@ def predict(self, X: utils.ArrayType) -> bpd.DataFrame:
             if self.model_path is None:
                 raise ValueError("Model GCS path must be provided.")
             self._bqml_model = self._create_bqml_model()
-        self._bqml_model = cast(core.BqmlModel, self._bqml_model)
+        self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
 
         (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session)
 
@@ -178,7 +179,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> ONNXModel:
             if self.model_path is None:
                 raise ValueError("Model GCS path must be provided.")
             self._bqml_model = self._create_bqml_model()
-        self._bqml_model = cast(core.BqmlModel, self._bqml_model)
+        self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
 
         new_model = self._bqml_model.copy(model_name, replace)
         return new_model.session.read_gbq_model(model_name)
@@ -276,7 +277,7 @@ def predict(self, X: utils.ArrayType) -> bpd.DataFrame:
             if self.model_path is None:
                 raise ValueError("Model GCS path must be provided.")
             self._bqml_model = self._create_bqml_model()
-        self._bqml_model = cast(core.BqmlModel, self._bqml_model)
+        self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
 
         (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session)
 
@@ -297,7 +298,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBoostModel:
             if self.model_path is None:
                 raise ValueError("Model GCS path must be provided.")
             self._bqml_model = self._create_bqml_model()
-        self._bqml_model = cast(core.BqmlModel, self._bqml_model)
+        self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
 
         new_model = self._bqml_model.copy(model_name, replace)
         return new_model.session.read_gbq_model(model_name)

bigframes/ml/llm.py

@@ -16,7 +16,8 @@
 
 from __future__ import annotations
 
-from typing import cast, Iterable, Literal, Mapping, Optional, Union
+import typing
+from typing import Iterable, Literal, Mapping, Optional, Union
 import warnings
 
 import bigframes_vendored.constants as constants
@@ -252,7 +253,7 @@ def predict(
 
         if len(X.columns) == 1:
             # BQML identified the column by name
-            col_label = cast(blocks.Label, X.columns[0])
+            col_label = typing.cast(blocks.Label, X.columns[0])
             X = X.rename(columns={col_label: "content"})
 
         options: dict = {}
@@ -391,7 +392,7 @@ def predict(
 
         if len(X.columns) == 1:
             # BQML identified the column by name
-            col_label = cast(blocks.Label, X.columns[0])
+            col_label = typing.cast(blocks.Label, X.columns[0])
             X = X.rename(columns={col_label: "content"})
 
         # TODO(garrettwu): remove transform to ObjRefRuntime when BQML supports ObjRef as input
@@ -604,7 +605,10 @@ def fit(
         options["prompt_col"] = X.columns.tolist()[0]
 
         self._bqml_model = self._bqml_model_factory.create_llm_remote_model(
-            X, y, options=options, connection_name=cast(str, self.connection_name)
+            X,
+            y,
+            options=options,
+            connection_name=typing.cast(str, self.connection_name),
         )
         return self
 
@@ -735,7 +739,7 @@ def predict(
 
         if len(X.columns) == 1:
             # BQML identified the column by name
-            col_label = cast(blocks.Label, X.columns[0])
+            col_label = typing.cast(blocks.Label, X.columns[0])
             X = X.rename(columns={col_label: "prompt"})
 
         options: dict = {
@@ -820,8 +824,8 @@ def score(
             )
 
         # BQML identified the column by name
-        X_col_label = cast(blocks.Label, X.columns[0])
-        y_col_label = cast(blocks.Label, y.columns[0])
+        X_col_label = typing.cast(blocks.Label, X.columns[0])
+        y_col_label = typing.cast(blocks.Label, y.columns[0])
         X = X.rename(columns={X_col_label: "input_text"})
         y = y.rename(columns={y_col_label: "output_text"})
 
@@ -1033,7 +1037,7 @@ def predict(
 
         if len(X.columns) == 1:
             # BQML identified the column by name
-            col_label = cast(blocks.Label, X.columns[0])
+            col_label = typing.cast(blocks.Label, X.columns[0])
             X = X.rename(columns={col_label: "prompt"})
 
         options = {

bigframes/ml/model_selection.py

@@ -20,7 +20,8 @@
 import inspect
 from itertools import chain
 import time
-from typing import cast, Generator, List, Optional, Union
+import typing
+from typing import Generator, List, Optional, Union
 
 import bigframes_vendored.sklearn.model_selection._split as vendored_model_selection_split
 import bigframes_vendored.sklearn.model_selection._validation as vendored_model_selection_validation
@@ -99,10 +100,10 @@ def _stratify_split(df: bpd.DataFrame, stratify: bpd.Series) -> List[bpd.DataFra
             train_dfs.append(train)
             test_dfs.append(test)
 
-        train_df = cast(
+        train_df = typing.cast(
             bpd.DataFrame, bpd.concat(train_dfs).drop(columns="bigframes_stratify_col")
         )
-        test_df = cast(
+        test_df = typing.cast(
             bpd.DataFrame, bpd.concat(test_dfs).drop(columns="bigframes_stratify_col")
         )
         return [train_df, test_df]

bigframes/ml/preprocessing.py

@@ -18,7 +18,7 @@
 from __future__ import annotations
 
 import typing
-from typing import cast, Iterable, List, Literal, Optional, Union
+from typing import Iterable, List, Literal, Optional, Union
 
 import bigframes_vendored.sklearn.preprocessing._data
 import bigframes_vendored.sklearn.preprocessing._discretization
@@ -470,7 +470,7 @@ def _parse_from_sql(cls, sql: str) -> tuple[OneHotEncoder, str]:
         s = sql[sql.find("(") + 1 : sql.find(")")]
         col_label, drop_str, top_k, frequency_threshold = s.split(", ")
         drop = (
-            cast(Literal["most_frequent"], "most_frequent")
+            typing.cast(Literal["most_frequent"], "most_frequent")
             if drop_str.lower() == "'most_frequent'"
             else None
         )