How to generate a train-test-split based on a group id?

Question

I have the following data:

pd.DataFrame({'Group_ID':[1,1,1,2,2,2,3,4,5,5],
          'Item_id':[1,2,3,4,5,6,7,8,9,10],
          'Target': [0,0,1,0,1,1,0,0,0,1]})

   Group_ID Item_id Target
0         1       1      0
1         1       2      0
2         1       3      1
3         2       4      0
4         2       5      1
5         2       6      1
6         3       7      0
7         4       8      0
8         5       9      0
9         5      10      1

I need to split the dataset into a training and testing set based on the "Group_ID" so that 80% of the data goes into a training set and 20% into a test set.

That is, I need my training set to look something like:

    Group_ID Item_id Target
0          1       1      0
1          1       2      0
2          1       3      1
3          2       4      0
4          2       5      1
5          2       6      1
6          3       7      0
7          4       8      0

And test set:

Test Set
   Group_ID Item_id Target
8         5       9      0
9         5      10      1

What would be the simplest way to do this? As far as I know, the standard test_train_split function in sklearn does not support splitting by groups in a way where I can also indicate the size of the split (e.g. 80/20).

Answer 1

I figured out the answer. This seems to work:

splitter = GroupShuffleSplit(test_size=.20, n_splits=2, random_state = 7)
split = splitter.split(df, groups=df['Group_Id'])
train_inds, test_inds = next(split)

train = df.iloc[train_inds]
test = df.iloc[test_inds]