基于组变量训练测试拆分sklearn

Question

My X is as follows: EDIT1:

Unique ID.   Exp start date.   Value.    Status.
001          01/01/2020.       4000.     Closed
001          12/01/2019        4000.     Archived
002          01/01/2020.       5000.     Closed
002          12/01/2019        5000.     Archived

I want to make sure that none of the unique IDs that were in training are included in testing. I am using sklearn test train split. Is this possible?

Answer 1

I believe you need GroupShuffleSplit ( documentation here ).

import numpy as np
from sklearn.model_selection import GroupShuffleSplit
X = np.ones(shape=(8, 2))
y = np.ones(shape=(8, 1))
groups = np.array([1, 1, 2, 2, 2, 3, 3, 3])
print(groups.shape)

gss = GroupShuffleSplit(n_splits=2, train_size=.7, random_state=42)

for train_idx, test_idx in gss.split(X, y, groups):
    print("TRAIN:", train_idx, "TEST:", test_idx)

TRAIN: [2 3 4 5 6 7] TEST: [0 1]
TRAIN: [0 1 5 6 7] TEST: [2 3 4]

It can be seen from above that train/test indices are created based on the groups variable.

In your case, Unique ID. should be used as groups.

Answer 2

Good for you that train_test_split has the stratify parameter. if you set it to X['Unique ID'] , it means there is no way you can find a unique id in both training and testing set.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, stratify=df['Unique ID'].values)