Particionamiento con LeavePOut

• Ultima modificación: 2023-02-27 | `YouTube <>`__

from sklearn.model_selection import LeavePOut

leavePOut = LeavePOut(
    # --------------------------------------------------------------------------
    # Tamaño del conjunto de salida
    p=3,
)

leavePOut

LeavePOut(p=3)

from mymodule import plot_schema

y_classes = [0] * 10 + [1] * 10

plot_schema(leavePOut, y_classes)

leavePOut.get_n_splits(y_classes)

1140

import numpy as np

X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
y = np.array([1, 2, 3, 4, 5, 6])

leavePOut = LeavePOut(p=2)

for i, (train_index, test_index) in enumerate(leavePOut.split(X)):
    print(f"Fold {i}:")
    print(f"  Train: index={train_index}")
    print(f"  Test:  index={test_index}")
    print()

Fold 0:
  Train: index=[2 3 4 5]
  Test:  index=[0 1]

Fold 1:
  Train: index=[1 3 4 5]
  Test:  index=[0 2]

Fold 2:
  Train: index=[1 2 4 5]
  Test:  index=[0 3]

Fold 3:
  Train: index=[1 2 3 5]
  Test:  index=[0 4]

Fold 4:
  Train: index=[1 2 3 4]
  Test:  index=[0 5]

Fold 5:
  Train: index=[0 3 4 5]
  Test:  index=[1 2]

Fold 6:
  Train: index=[0 2 4 5]
  Test:  index=[1 3]

Fold 7:
  Train: index=[0 2 3 5]
  Test:  index=[1 4]

Fold 8:
  Train: index=[0 2 3 4]
  Test:  index=[1 5]

Fold 9:
  Train: index=[0 1 4 5]
  Test:  index=[2 3]

Fold 10:
  Train: index=[0 1 3 5]
  Test:  index=[2 4]

Fold 11:
  Train: index=[0 1 3 4]
  Test:  index=[2 5]

Fold 12:
  Train: index=[0 1 2 5]
  Test:  index=[3 4]

Fold 13:
  Train: index=[0 1 2 4]
  Test:  index=[3 5]

Fold 14:
  Train: index=[0 1 2 3]
  Test:  index=[4 5]