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This repository was archived by the owner on May 7, 2026. It is now read-only.

Commit ad0f33c

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GarrettWuGarrettWu
authored
docs: fix cast method shown on public docs (#2436)
Thank you for opening a Pull Request! Before submitting your PR, there are a few things you can do to make sure it goes smoothly: - [ ] Make sure to open an issue as a [bug/issue](https://github.com/googleapis/python-bigquery-dataframes/issues/new/choose) before writing your code! That way we can discuss the change, evaluate designs, and agree on the general idea - [ ] Ensure the tests and linter pass - [ ] Code coverage does not decrease (if any source code was changed) - [ ] Appropriate docs were updated (if necessary) Fixes #<issue_number_goes_here> 🦕
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+37-29Lines changed: 37 additions & 29 deletions

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‎bigframes/ml/base.py‎

Copy file name to clipboardExpand all lines: bigframes/ml/base.py
+5-4Lines changed: 5 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -24,7 +24,8 @@
2424
"""
2525

2626
import abc
27-
from typing import cast, Optional, TypeVar, Union
27+
import typing
28+
from typing import Optional, TypeVar, Union
2829
import warnings
2930

3031
import bigframes_vendored.sklearn.base
@@ -133,7 +134,7 @@ def register(self: _T, vertex_ai_model_id: Optional[str] = None) -> _T:
133134
self._bqml_model = self._create_bqml_model() # type: ignore
134135
except AttributeError:
135136
raise RuntimeError("A model must be trained before register.")
136-
self._bqml_model = cast(core.BqmlModel, self._bqml_model)
137+
self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
137138

138139
self._bqml_model.register(vertex_ai_model_id)
139140
return self
@@ -286,7 +287,7 @@ def _predict_and_retry(
286287
bpd.concat([df_result, df_succ]) if df_result is not None else df_succ
287288
)
288289

289-
df_result = cast(
290+
df_result = typing.cast(
290291
bpd.DataFrame,
291292
bpd.concat([df_result, df_fail]) if df_result is not None else df_fail,
292293
)
@@ -306,7 +307,7 @@ def _extract_output_names(self):
306307

307308
output_names = []
308309
for transform_col in self._bqml_model._model._properties["transformColumns"]:
309-
transform_col_dict = cast(dict, transform_col)
310+
transform_col_dict = typing.cast(dict, transform_col)
310311
# pass the columns that are not transformed
311312
if "transformSql" not in transform_col_dict:
312313
continue
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‎bigframes/ml/compose.py‎

Copy file name to clipboardExpand all lines: bigframes/ml/compose.py
+3-3Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -21,7 +21,7 @@
2121
import re
2222
import types
2323
import typing
24-
from typing import cast, Iterable, List, Optional, Set, Tuple, Union
24+
from typing import Iterable, List, Optional, Set, Tuple, Union
2525

2626
from bigframes_vendored import constants
2727
import bigframes_vendored.sklearn.compose._column_transformer
@@ -218,7 +218,7 @@ def camel_to_snake(name):
218218

219219
output_names = []
220220
for transform_col in bq_model._properties["transformColumns"]:
221-
transform_col_dict = cast(dict, transform_col)
221+
transform_col_dict = typing.cast(dict, transform_col)
222222
# pass the columns that are not transformed
223223
if "transformSql" not in transform_col_dict:
224224
continue
@@ -282,7 +282,7 @@ def _merge(
282282
return self # SQLScalarColumnTransformer only work inside ColumnTransformer
283283
feature_columns_sorted = sorted(
284284
[
285-
cast(str, feature_column.name)
285+
typing.cast(str, feature_column.name)
286286
for feature_column in bq_model.feature_columns
287287
]
288288
)
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‎bigframes/ml/core.py‎

Copy file name to clipboardExpand all lines: bigframes/ml/core.py
+3-2Lines changed: 3 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -18,7 +18,8 @@
1818

1919
import dataclasses
2020
import datetime
21-
from typing import Callable, cast, Iterable, Mapping, Optional, Union
21+
import typing
22+
from typing import Callable, Iterable, Mapping, Optional, Union
2223
import uuid
2324

2425
from google.cloud import bigquery
@@ -376,7 +377,7 @@ def copy(self, new_model_name: str, replace: bool = False) -> BqmlModel:
376377
def register(self, vertex_ai_model_id: Optional[str] = None) -> BqmlModel:
377378
if vertex_ai_model_id is None:
378379
# vertex id needs to start with letters. https://cloud.google.com/vertex-ai/docs/general/resource-naming
379-
vertex_ai_model_id = "bigframes_" + cast(str, self._model.model_id)
380+
vertex_ai_model_id = "bigframes_" + typing.cast(str, self._model.model_id)
380381

381382
# truncate as Vertex ID only accepts 63 characters, easily exceeding the limit for temp models.
382383
# The possibility of conflicts should be low.
Collapse file

‎bigframes/ml/imported.py‎

Copy file name to clipboardExpand all lines: bigframes/ml/imported.py
+8-7Lines changed: 8 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -16,7 +16,8 @@
1616

1717
from __future__ import annotations
1818

19-
from typing import cast, Mapping, Optional
19+
import typing
20+
from typing import Mapping, Optional
2021

2122
from google.cloud import bigquery
2223

@@ -78,7 +79,7 @@ def predict(self, X: utils.ArrayType) -> bpd.DataFrame:
7879
if self.model_path is None:
7980
raise ValueError("Model GCS path must be provided.")
8081
self._bqml_model = self._create_bqml_model()
81-
self._bqml_model = cast(core.BqmlModel, self._bqml_model)
82+
self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
8283

8384
(X,) = utils.batch_convert_to_dataframe(X)
8485

@@ -99,7 +100,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> TensorFlowModel:
99100
if self.model_path is None:
100101
raise ValueError("Model GCS path must be provided.")
101102
self._bqml_model = self._create_bqml_model()
102-
self._bqml_model = cast(core.BqmlModel, self._bqml_model)
103+
self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
103104

104105
new_model = self._bqml_model.copy(model_name, replace)
105106
return new_model.session.read_gbq_model(model_name)
@@ -157,7 +158,7 @@ def predict(self, X: utils.ArrayType) -> bpd.DataFrame:
157158
if self.model_path is None:
158159
raise ValueError("Model GCS path must be provided.")
159160
self._bqml_model = self._create_bqml_model()
160-
self._bqml_model = cast(core.BqmlModel, self._bqml_model)
161+
self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
161162

162163
(X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session)
163164

@@ -178,7 +179,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> ONNXModel:
178179
if self.model_path is None:
179180
raise ValueError("Model GCS path must be provided.")
180181
self._bqml_model = self._create_bqml_model()
181-
self._bqml_model = cast(core.BqmlModel, self._bqml_model)
182+
self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
182183

183184
new_model = self._bqml_model.copy(model_name, replace)
184185
return new_model.session.read_gbq_model(model_name)
@@ -276,7 +277,7 @@ def predict(self, X: utils.ArrayType) -> bpd.DataFrame:
276277
if self.model_path is None:
277278
raise ValueError("Model GCS path must be provided.")
278279
self._bqml_model = self._create_bqml_model()
279-
self._bqml_model = cast(core.BqmlModel, self._bqml_model)
280+
self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
280281

281282
(X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session)
282283

@@ -297,7 +298,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBoostModel:
297298
if self.model_path is None:
298299
raise ValueError("Model GCS path must be provided.")
299300
self._bqml_model = self._create_bqml_model()
300-
self._bqml_model = cast(core.BqmlModel, self._bqml_model)
301+
self._bqml_model = typing.cast(core.BqmlModel, self._bqml_model)
301302

302303
new_model = self._bqml_model.copy(model_name, replace)
303304
return new_model.session.read_gbq_model(model_name)
Collapse file

‎bigframes/ml/llm.py‎

Copy file name to clipboardExpand all lines: bigframes/ml/llm.py
+12-8Lines changed: 12 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -16,7 +16,8 @@
1616

1717
from __future__ import annotations
1818

19-
from typing import cast, Iterable, Literal, Mapping, Optional, Union
19+
import typing
20+
from typing import Iterable, Literal, Mapping, Optional, Union
2021
import warnings
2122

2223
import bigframes_vendored.constants as constants
@@ -252,7 +253,7 @@ def predict(
252253

253254
if len(X.columns) == 1:
254255
# BQML identified the column by name
255-
col_label = cast(blocks.Label, X.columns[0])
256+
col_label = typing.cast(blocks.Label, X.columns[0])
256257
X = X.rename(columns={col_label: "content"})
257258

258259
options: dict = {}
@@ -391,7 +392,7 @@ def predict(
391392

392393
if len(X.columns) == 1:
393394
# BQML identified the column by name
394-
col_label = cast(blocks.Label, X.columns[0])
395+
col_label = typing.cast(blocks.Label, X.columns[0])
395396
X = X.rename(columns={col_label: "content"})
396397

397398
# TODO(garrettwu): remove transform to ObjRefRuntime when BQML supports ObjRef as input
@@ -604,7 +605,10 @@ def fit(
604605
options["prompt_col"] = X.columns.tolist()[0]
605606

606607
self._bqml_model = self._bqml_model_factory.create_llm_remote_model(
607-
X, y, options=options, connection_name=cast(str, self.connection_name)
608+
X,
609+
y,
610+
options=options,
611+
connection_name=typing.cast(str, self.connection_name),
608612
)
609613
return self
610614

@@ -735,7 +739,7 @@ def predict(
735739

736740
if len(X.columns) == 1:
737741
# BQML identified the column by name
738-
col_label = cast(blocks.Label, X.columns[0])
742+
col_label = typing.cast(blocks.Label, X.columns[0])
739743
X = X.rename(columns={col_label: "prompt"})
740744

741745
options: dict = {
@@ -820,8 +824,8 @@ def score(
820824
)
821825

822826
# BQML identified the column by name
823-
X_col_label = cast(blocks.Label, X.columns[0])
824-
y_col_label = cast(blocks.Label, y.columns[0])
827+
X_col_label = typing.cast(blocks.Label, X.columns[0])
828+
y_col_label = typing.cast(blocks.Label, y.columns[0])
825829
X = X.rename(columns={X_col_label: "input_text"})
826830
y = y.rename(columns={y_col_label: "output_text"})
827831

@@ -1033,7 +1037,7 @@ def predict(
10331037

10341038
if len(X.columns) == 1:
10351039
# BQML identified the column by name
1036-
col_label = cast(blocks.Label, X.columns[0])
1040+
col_label = typing.cast(blocks.Label, X.columns[0])
10371041
X = X.rename(columns={col_label: "prompt"})
10381042

10391043
options = {
Collapse file

‎bigframes/ml/model_selection.py‎

Copy file name to clipboardExpand all lines: bigframes/ml/model_selection.py
+4-3Lines changed: 4 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -20,7 +20,8 @@
2020
import inspect
2121
from itertools import chain
2222
import time
23-
from typing import cast, Generator, List, Optional, Union
23+
import typing
24+
from typing import Generator, List, Optional, Union
2425

2526
import bigframes_vendored.sklearn.model_selection._split as vendored_model_selection_split
2627
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
99100
train_dfs.append(train)
100101
test_dfs.append(test)
101102

102-
train_df = cast(
103+
train_df = typing.cast(
103104
bpd.DataFrame, bpd.concat(train_dfs).drop(columns="bigframes_stratify_col")
104105
)
105-
test_df = cast(
106+
test_df = typing.cast(
106107
bpd.DataFrame, bpd.concat(test_dfs).drop(columns="bigframes_stratify_col")
107108
)
108109
return [train_df, test_df]
Collapse file

‎bigframes/ml/preprocessing.py‎

Copy file name to clipboardExpand all lines: bigframes/ml/preprocessing.py
+2-2Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -18,7 +18,7 @@
1818
from __future__ import annotations
1919

2020
import typing
21-
from typing import cast, Iterable, List, Literal, Optional, Union
21+
from typing import Iterable, List, Literal, Optional, Union
2222

2323
import bigframes_vendored.sklearn.preprocessing._data
2424
import bigframes_vendored.sklearn.preprocessing._discretization
@@ -470,7 +470,7 @@ def _parse_from_sql(cls, sql: str) -> tuple[OneHotEncoder, str]:
470470
s = sql[sql.find("(") + 1 : sql.find(")")]
471471
col_label, drop_str, top_k, frequency_threshold = s.split(", ")
472472
drop = (
473-
cast(Literal["most_frequent"], "most_frequent")
473+
typing.cast(Literal["most_frequent"], "most_frequent")
474474
if drop_str.lower() == "'most_frequent'"
475475
else None
476476
)

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