mapie.subsample
.BlockBootstrap¶
- class mapie.subsample.BlockBootstrap(n_resamplings: int = 30, length: Optional[int] = None, n_blocks: Optional[int] = None, overlapping: bool = False, random_state: Optional[Union[int, RandomState]] = None)[source]¶
Generate a sampling method, that block bootstraps the training set. It can replace KFold, LeaveOneOut or SubSample as cv argument in the MapieRegressor class.
- Parameters
- n_resamplingsint
Number of resamplings. By default
30
.- length: int
Length of the blocks. By default
None
, the length of the training set divided byn_blocks
.- overlapping: bool
Whether the blocks can overlap or not. By default
False
.- n_blocks: int
Number of blocks in each resampling. By default
None
, the size of the training set divided bylength
.- random_state: Optional
int or RandomState instance.
- Raises
- ValueError
If both
length
andn_blocks
areNone
.
Examples
>>> import numpy as np >>> from mapie.subsample import BlockBootstrap >>> cv = BlockBootstrap(n_resamplings=2, length=3, random_state=0) >>> X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) >>> for train_index, test_index in cv.split(X): ... print(f"train index is {train_index}, test index is {test_index}") train index is [1 2 3 4 5 6 1 2 3 4 5 6], test index is [8 9 7] train index is [4 5 6 7 8 9 1 2 3 7 8 9], test index is []
- __init__(n_resamplings: int = 30, length: Optional[int] = None, n_blocks: Optional[int] = None, overlapping: bool = False, random_state: Optional[Union[int, RandomState]] = None) None [source]¶
- get_n_splits(*args: Any, **kargs: Any) int [source]¶
Returns the number of splitting iterations in the cross-validator.
- Returns
- int
Returns the number of splitting iterations in the cross-validator.
- split(X: ndarray[Any, dtype[_ScalarType_co]], *args: Any, **kargs: Any) Generator[Tuple[ndarray[Any, dtype[_ScalarType_co]], ndarray[Any, dtype[_ScalarType_co]]], None, None] [source]¶
Generate indices to split data into training and test sets.
- Parameters
- XNDArray of shape (n_samples, n_features)
Training data.
- Yields
- trainNDArray of shape (n_indices_training,)
The training set indices for that split.
- testNDArray of shape (n_indices_test,)
The testing set indices for that split.
- Raises
- ValueError
If
length
is not positive or greater than the train set size.