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Functions for test/train split and model tuning. This module is styled after scikit-learn's model_selection module: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection.
Classes KFoldKFold(n_splits: int = 5, *, random_state: typing.Optional[int] = None)K-Fold cross-validator.
Split data in train/test sets. Split dataset into k consecutive folds.
Each fold is then used once as a validation while the k - 1 remaining folds form the training set.
Examples:
>>> import bigframes.pandas as bpd
>>> from bigframes.ml.model_selection import KFold
>>> bpd.options.display.progress_bar = None
>>> X = bpd.DataFrame({"feat0": [1, 3, 5], "feat1": [2, 4, 6]})
>>> y = bpd.DataFrame({"label": [1, 2, 3]})
>>> kf = KFold(n_splits=3, random_state=42)
>>> for i, (X_train, X_test, y_train, y_test) in enumerate(kf.split(X, y)):
... print(f"Fold {i}:")
... print(f" X_train: {X_train}")
... print(f" X_test: {X_test}")
... print(f" y_train: {y_train}")
... print(f" y_test: {y_test}")
...
Fold 0:
X_train: feat0 feat1
1 3 4
2 5 6
<BLANKLINE>
[2 rows x 2 columns]
X_test: feat0 feat1
0 1 2
<BLANKLINE>
[1 rows x 2 columns]
y_train: label
1 2
2 3
<BLANKLINE>
[2 rows x 1 columns]
y_test: label
0 1
<BLANKLINE>
[1 rows x 1 columns]
Fold 1:
X_train: feat0 feat1
0 1 2
2 5 6
<BLANKLINE>
[2 rows x 2 columns]
X_test: feat0 feat1
1 3 4
<BLANKLINE>
[1 rows x 2 columns]
y_train: label
0 1
2 3
<BLANKLINE>
[2 rows x 1 columns]
y_test: label
1 2
<BLANKLINE>
[1 rows x 1 columns]
Fold 2:
X_train: feat0 feat1
0 1 2
1 3 4
<BLANKLINE>
[2 rows x 2 columns]
X_test: feat0 feat1
2 5 6
<BLANKLINE>
[1 rows x 2 columns]
y_train: label
0 1
1 2
<BLANKLINE>
[2 rows x 1 columns]
y_test: label
2 3
<BLANKLINE>
[1 rows x 1 columns]
n_splits int
Number of folds. Must be at least 2. Default to 5.
random_state Optional[int]
A seed to use for randomly choosing the rows of the split. If not set, a random split will be generated each time. Default to None.
Modules Functions cross_validatecross_validate(
estimator,
X: typing.Union[
bigframes.dataframe.DataFrame,
bigframes.series.Series,
pandas.core.frame.DataFrame,
pandas.core.series.Series,
],
y: typing.Optional[
typing.Union[
bigframes.dataframe.DataFrame,
bigframes.series.Series,
pandas.core.frame.DataFrame,
pandas.core.series.Series,
]
] = None,
*,
cv: typing.Optional[typing.Union[int, bigframes.ml.model_selection.KFold]] = None
) -> dict[str, list]Evaluate metric(s) by cross-validation and also record fit/score times.
Examples:
>>> import bigframes.pandas as bpd
>>> from bigframes.ml.model_selection import cross_validate, KFold
>>> from bigframes.ml.linear_model import LinearRegression
>>> bpd.options.display.progress_bar = None
>>> X = bpd.DataFrame({"feat0": [1, 3, 5], "feat1": [2, 4, 6]})
>>> y = bpd.DataFrame({"label": [1, 2, 3]})
>>> model = LinearRegression()
>>> scores = cross_validate(model, X, y, cv=3) # doctest: +SKIP
>>> for score in scores["test_score"]: # doctest: +SKIP
... print(score["mean_squared_error"][0])
...
5.218167286047954e-19
2.726229944928669e-18
1.6197635612324266e-17
Dict[str, List] A dict of arrays containing the score/time arrays for each scorer is returned. The keys for this dict are: test_score The score array for test scores on each cv split. fit_time The time for fitting the estimator on the train set for each cv split. score_time The time for scoring the estimator on the test set for each cv split. train_test_split
train_test_split(
*arrays: typing.Union[
bigframes.dataframe.DataFrame,
bigframes.series.Series,
pandas.core.frame.DataFrame,
pandas.core.series.Series,
],
test_size: typing.Optional[float] = None,
train_size: typing.Optional[float] = None,
random_state: typing.Optional[int] = None,
stratify: typing.Optional[bigframes.series.Series] = None
) -> typing.List[typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]]Splits dataframes or series into random train and test subsets.
Examples:
>>> import bigframes.pandas as bpd
>>> from bigframes.ml.model_selection import train_test_split
>>> bpd.options.display.progress_bar = None
>>> X = bpd.DataFrame({"feat0": [0, 2, 4, 6, 8], "feat1": [1, 3, 5, 7, 9]})
>>> y = bpd.DataFrame({"label": [0, 1, 2, 3, 4]})
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
>>> X_train
feat0 feat1
0 0 1
1 2 3
4 8 9
<BLANKLINE>
[3 rows x 2 columns]
>>> y_train
label
0 0
1 1
4 4
<BLANKLINE>
[3 rows x 1 columns]
>>> X_test
feat0 feat1
2 4 5
3 6 7
<BLANKLINE>
[2 rows x 2 columns]
>>> y_test
label
2 2
3 3
<BLANKLINE>
[2 rows x 1 columns]
\*arrays bigframes.dataframe.DataFrame or bigframes.series.Series
A sequence of BigQuery DataFrames or Series that can be joined on their indexes.
test_size default None
The proportion of the dataset to include in the test split. If None, this will default to the complement of train_size. If both are none, it will be set to 0.25.
train_size default None
The proportion of the dataset to include in the train split. If None, this will default to the complement of test_size.
random_state default None
A seed to use for randomly choosing the rows of the split. If not set, a random split will be generated each time.
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Last updated 2025-08-12 UTC.
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