test/train splits in pycaret using a column for grouping rows that should be in the same split - scikit-learn

Question

My dataset contains a column with some data I need to use for splitting by groups in a way that rows belonging to same group should not be divided into train/test but sent as a whole to one of the splits using PYCARET
10 row sample for clarification:
group_id measure1 measure2 measure3
1 3455 3425 345
1 6455 825 945
1 6444 225 145
2 23 34 233
2 623 22 888
3 3455 3425 345
3 6155 525 645
3 6434 325 845
4 93 345 233
4 693 222 808
every unique group_id should be sent to any split in full this way (using 80/20):
TRAIN SET:
group_id measure1 measure2 measure3
1 3455 3425 345
1 6455 825 945
1 6444 225 145
3 3455 3425 345
3 6155 525 645
3 6434 325 845
4 93 345 233
4 693 222 808
TEST SET:
group_id measure1 measure2 measure3
2 23 34 233
2 623 22 888

Answer 1

You can try the following per the documentation
https://pycaret.readthedocs.io/en/latest/api/classification.html
fold_strategy = "groupkfold"

Answer 2

One solution could look like this:
import numpy as np
import pandas as pd
from itertools import combinations
def is_possible_sum(numbers, n):
for r in range(len(numbers)):
for combo in combinations(numbers, r + 1):
if sum(combo) == n:
return combo
print(f'Desired split not possible')
raise ArithmeticError
def train_test_split(table: pd.DataFrame, train_fraction: float, col_identifier: str):
train_ids = []
occurrences = table[col_identifier].value_counts().to_dict()
required = sum(occurrences.values()) * train_fraction
lengths = is_possible_sum(occurrences.values(), required)
for i in lengths:
for key, value in occurrences.items():
if value == i:
train_ids.append(key)
del occurrences[key] # prevents the same ID from being selected twice
break
train = table[table[col_identifier].isin(train_ids)]
test = table[~table[col_identifier].isin(train_ids)]
return train, test
if __name__ == '__main__':
df = pd.DataFrame()
df['Group_ID'] = np.array([1, 1, 1, 2, 2, 3, 3, 3, 4, 4])
df['Measurement'] = np.random.random(10)
train_part, test_part = train_test_split(df, 0.8, 'Group_ID')
Some remarks:
This is probably the least elegant way to do it...
It uses an ungodly amount of for loops and is probably slow for larger dataframes. It also doesn't randomize the split.
Lots of this is because the dictionary of group_id and the count of the samples with a certain group_id can't be reversed as some entries might be ambiguous. You could probably do this with numpy arrays as well, but I doubt that the overall structure would be much different.
First function taken from here: How to check if a sum is possible in array?