mapie.subsample
.Subsample¶
- class mapie.subsample.Subsample(n_resamplings: int = 30, n_samples: Optional[Union[int, float]] = None, replace: bool = True, random_state: Optional[Union[int, RandomState]] = None)[source]¶
Generate a sampling method, that resamples the training set with possible bootstraps. It can replace KFold or LeaveOneOut as cv argument in the MAPIE class.
- Parameters
- n_resamplingsint
Number of resamplings. By default
30
.- n_samples: Union[int, float]
Number of samples in each resampling. By default
None
, the size of the training set. If it is between 0 and 1, it becomes the fraction of samples- replace: bool
Whether to replace samples in resamplings or not. By default
True
.- random_state: Optional[Union[int, RandomState]]
int or RandomState instance. By default
None
Examples
>>> import numpy as np >>> from mapie.subsample import Subsample >>> cv = Subsample(n_resamplings=2,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 [5 0 3 3 7 9 3 5 2 4], test index is [1 6 8] train index is [7 6 8 8 1 6 7 7 8 1], test index is [0 2 3 4 5 9]
- __init__(n_resamplings: int = 30, n_samples: Optional[Union[int, float]] = None, replace: bool = True, 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.