class rebalancedcv.RebalancedLeavePOut(p)
Rebalanced Leave-P-Out cross-validator.
Provides train/test indices to split data in train/test sets with subsampling within the training set to ensure that all training folds have identical class balances. This cross-validation tests on all distinct samples of size p, while a remaining n - 2p samples form the training set in each iteration, with an additional p samples used to subsamples from within the training set.
This class is designed to have the same functionality and
implementation structure as scikit-learn’s LeavePOut()
Note: Similarly to what was previously mentioned in scikit-learn’s documentation, RebalancedLeavePOut(p) is NOT equivalent to RebalancedKFold(n_splits=n_samples // p) which creates non-overlapping test sets. Due to the high number of iterations which grows combinatorically with the number of samples this cross-validation method can be very costly.
At least 1+p observations per class are needed for
RebalancedLeavePOut.
### Observing the indices on a small example dataset
import numpy as np
np.random.seed(1)
from rebalancedcv import RebalancedLeavePOut
X = np.array([[1, 2, 1, 2, 1, 2], [3, 4, 3, 4, 3, 4]]).T
y = np.array([0,1,0,1,0,1])
rloo = RebalancedLeavePOut(p=2)
for i, (train_index, test_index) in enumerate(rloo.split(X, y)):
print(f"Fold {i}:")
print(f" Train: index={train_index}")
print(f" Test: index={test_index}")Fold 0:
Train: index=[4 5]
Test: index=[0 1]
Fold 1:
Train: index=[1 4]
Test: index=[0 2]
Fold 2:
Train: index=[4 5]
Test: index=[0 3]
Fold 3:
Train: index=[2 3]
Test: index=[0 4]
Fold 4:
Train: index=[1 2]
Test: index=[0 5]
Fold 5:
Train: index=[3 4]
Test: index=[1 2]
Fold 6:
Train: index=[2 5]
Test: index=[1 3]
Fold 7:
Train: index=[0 5]
Test: index=[1 4]
Fold 8:
Train: index=[3 4]
Test: index=[1 5]
Fold 9:
Train: index=[0 5]
Test: index=[2 3]
Fold 10:
Train: index=[0 5]
Test: index=[2 4]
Fold 11:
Train: index=[1 4]
Test: index=[2 5]
Fold 12:
Train: index=[0 1]
Test: index=[3 4]
Fold 13:
Train: index=[1 2]
Test: index=[3 5]
Fold 14:
Train: index=[0 1]
Test: index=[4 5]The methods of the RebalancedLeavePOut class are
designed to enable identical funcitonality to scikit-learn’s LeavePOut.
n_samples is the number of samples
and n_features is the number of features.n_samples is the number of samples
and n_features is the number of features.