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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
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,
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.
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 |
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,
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.
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. |