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 @@
 """
 
 importabc
-fromtypingimportcast, Optional, TypeVar, Union
+importtyping
+fromtypingimportOptional, TypeVar, Union
 importwarnings
 
 importbigframes_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
 exceptAttributeError:
 raiseRuntimeError("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)
 returnself
@@ -286,7 +287,7 @@ def _predict_and_retry(
 bpd.concat([df_result, df_succ]) ifdf_resultisnotNoneelsedf_succ
  )
 
-df_result=cast(
+df_result=typing.cast(
 bpd.DataFrame,
 bpd.concat([df_result, df_fail]) ifdf_resultisnotNoneelsedf_fail,
  )
@@ -306,7 +307,7 @@ def _extract_output_names(self):
 
 output_names= []
 fortransform_colinself._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"notintransform_col_dict:
 continue

bigframes/ml/compose.py

@@ -21,7 +21,7 @@
 importre
 importtypes
 importtyping
-fromtypingimportcast, Iterable, List, Optional, Set, Tuple, Union
+fromtypingimportIterable, List, Optional, Set, Tuple, Union
 
 frombigframes_vendoredimportconstants
 importbigframes_vendored.sklearn.compose._column_transformer
@@ -218,7 +218,7 @@ def camel_to_snake(name):
 
 output_names= []
 fortransform_colinbq_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"notintransform_col_dict:
 continue
@@ -282,7 +282,7 @@ def _merge(
 returnself# SQLScalarColumnTransformer only work inside ColumnTransformer
 feature_columns_sorted=sorted(
  [
-cast(str, feature_column.name)
+typing.cast(str, feature_column.name)
 forfeature_columninbq_model.feature_columns
  ]
  )

bigframes/ml/core.py

@@ -18,7 +18,8 @@
 
 importdataclasses
 importdatetime
-fromtypingimportCallable, cast, Iterable, Mapping, Optional, Union
+importtyping
+fromtypingimportCallable, Iterable, Mapping, Optional, Union
 importuuid
 
 fromgoogle.cloudimportbigquery
@@ -376,7 +377,7 @@ def copy(self, new_model_name: str, replace: bool = False) -> BqmlModel:
 defregister(self, vertex_ai_model_id: Optional[str] =None) ->BqmlModel:
 ifvertex_ai_model_idisNone:
 # 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__ importannotations
 
-fromtypingimportcast, Mapping, Optional
+importtyping
+fromtypingimportMapping, Optional
 
 fromgoogle.cloudimportbigquery
 
@@ -78,7 +79,7 @@ def predict(self, X: utils.ArrayType) -> bpd.DataFrame:
 ifself.model_pathisNone:
 raiseValueError("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:
 ifself.model_pathisNone:
 raiseValueError("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)
 returnnew_model.session.read_gbq_model(model_name)
@@ -157,7 +158,7 @@ def predict(self, X: utils.ArrayType) -> bpd.DataFrame:
 ifself.model_pathisNone:
 raiseValueError("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:
 ifself.model_pathisNone:
 raiseValueError("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)
 returnnew_model.session.read_gbq_model(model_name)
@@ -276,7 +277,7 @@ def predict(self, X: utils.ArrayType) -> bpd.DataFrame:
 ifself.model_pathisNone:
 raiseValueError("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:
 ifself.model_pathisNone:
 raiseValueError("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)
 returnnew_model.session.read_gbq_model(model_name)

bigframes/ml/llm.py

@@ -16,7 +16,8 @@
 
 from __future__ importannotations
 
-fromtypingimportcast, Iterable, Literal, Mapping, Optional, Union
+importtyping
+fromtypingimportIterable, Literal, Mapping, Optional, Union
 importwarnings
 
 importbigframes_vendored.constantsasconstants
@@ -252,7 +253,7 @@ def predict(
 
 iflen(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(
 
 iflen(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),
  )
 returnself
 
@@ -735,7 +739,7 @@ def predict(
 
 iflen(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(
 
 iflen(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 @@
 importinspect
 fromitertoolsimportchain
 importtime
-fromtypingimportcast, Generator, List, Optional, Union
+importtyping
+fromtypingimportGenerator, List, Optional, Union
 
 importbigframes_vendored.sklearn.model_selection._splitasvendored_model_selection_split
 importbigframes_vendored.sklearn.model_selection._validationasvendored_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__ importannotations
 
 importtyping
-fromtypingimportcast, Iterable, List, Literal, Optional, Union
+fromtypingimportIterable, List, Literal, Optional, Union
 
 importbigframes_vendored.sklearn.preprocessing._data
 importbigframes_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")
 ifdrop_str.lower() =="'most_frequent'"
 elseNone
  )