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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.
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]
Parameters
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.
Methods
get_n_splits
get_n_splits
()
-
> int
Returns the number of splitting iterations in the cross-validator.
int
split
split
(
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
,
)
-
> typing
.
Generator
[
tuple
[
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
,
NoneType
],
...
,
],
None
,
None
,
]
Generate indices to split data into training and test set.
X
bigframes.dataframe.DataFrame
or bigframes.series.Series
BigFrames DataFrame or Series of shape (n_samples, n_features) Training data, where n_samples
is the number of samples and n_features
is the number of features.
y
bigframes.dataframe.DataFrame
, bigframes.series.Series
or None :Yields: *X_train ( bigframes.dataframe.DataFrame
or bigframes.series.Series
)* -- The training data for that split. X_test ( bigframes.dataframe.DataFrame
or bigframes.series.Series
): The testing data for that split. y_train ( bigframes.dataframe.DataFrame
, bigframes.series.Series
or None): The training label for that split. y_test ( bigframes.dataframe.DataFrame
, bigframes.series.Series
or None): The testing label for that split.
BigFrames DataFrame, Series of shape (n_samples,) or None. The target variable for supervised learning problems. Default to None.
