Module model_selection (1.22.0)

Version 1.22.0

• 1.29.0 (latest)
• 1.28.0
• 1.27.0
• 1.26.0
• 1.25.0
• 1.24.0
• 1.22.0
• 1.21.0
• 1.20.0
• 1.19.0
• 1.18.0
• 1.17.0
• 1.16.0
• 1.15.0
• 1.14.0
• 1.13.0
• 1.12.0
• 1.11.1
• 1.10.0
• 1.9.0
• 1.8.0
• 1.7.0
• 1.6.0
• 1.5.0
• 1.4.0
• 1.3.0
• 1.2.0
• 1.1.0
• 1.0.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.1
• 0.19.2
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.1
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.0
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0

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

KFold

KFold(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.

Parameters
Name	Description
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_validate

cross_validate(
    estimator,
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.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.

Parameters
Name	Description
X	bigframes.dataframe.DataFrame or bigframes.series.Series The data to fit.
y	bigframes.dataframe.DataFrame, bigframes.series.Series or None The target variable to try to predict in the case of supe()rvised learning. Default to None.
cv	int, bigframes.ml.model_selection.KFold or None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - int, to specify the number of folds in a KFold, - bigframes.ml.model_selection.KFold instance.

Returns
Type	Description
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],
    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.

Parameters
Name	Description
\*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.

Returns
Type	Description
List[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]]	A list of BigQuery DataFrames or Series.