facet.validation.StratifiedBootstrapCV#

class facet.validation.StratifiedBootstrapCV(n_splits=1000, random_state=None)[source]#

Stratified bootstrapping cross-validation.

Generates CV splits by random sampling with replacement. The resulting training set is the same size as the total sample; the test set consists of all samples not included in the training set.

Sampling is stratified based on a series or 1d array of group labels in the target vector. Bootstrapping is carried out separately for each group.

Bases

BaseBootstrapCV

Metaclasses

ABCMeta

Parameters

n_splits (int) – number of splits to generate (default: 1000)
random_state (Union[int, RandomState, None]) – random state to initialise the random generator with (optional)

Method summary

`get_n_splits`	Return the number of splits generated by this cross-validator.
`split`	Generate indices to split data into training and test set.

Definitions

get_n_splits(X=None, y=None, groups=None)#

Return the number of splits generated by this cross-validator.

Parameters

X (Union[ndarray[Any, dtype[Any]], DataFrame, None]) – for compatibility only, not used
y (Union[ndarray[Any, dtype[Any]], Series, DataFrame, None]) – for compatibility only, not used
groups (Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]], None]) – for compatibility only, not used

Return type

int

Returns

the number of splits

split(X, y=None, groups=None)#

Generate indices to split data into training and test set.

Parameters

X (Union[ndarray[Any, dtype[Any]], DataFrame]) – features
y (Union[ndarray[Any, dtype[Any]], Series, DataFrame, None]) – target: target variable for supervised learning problems, used as labels for stratification
groups (Union[ndarray[Any, dtype[Any]], Series, DataFrame, None]) – ignored; exists for compatibility

Return type

Generator[Tuple[ndarray[Any, dtype[int64]], ndarray[Any, dtype[int64]]], None, None]

Returns

a generator yielding (train, test) tuples where train and test are numpy arrays with train and test indices, respectively

facet.validation.StationaryBootstrapCV

Tutorials