[BUG] Groups parameter is not handed over to inner cv

[LeSasse]

Describe the bug
A clear and concise description of what the bug is. Include the error message in detail.

When I try to run a GroupKFold CV in both the inner and outer cv, the inner cv does not seem to get the groups parameter.
Does this need to be handed over additionally in the search_params?

To Reproduce
Steps to reproduce the behavior:

I set up julearn on the new julearn_sk_pandas branch by running:

#!/usr/bin/bash

conda create -n example_venv python=3.9.15 seaborn 
eval "$(conda shell.bash hook)"
conda activate example_venv

# check python version as expected
if [ "$(python3 --version)" != "Python 3.9.15" ]; then
  echo "python version $(python3 --version) is not correct, should be 3.9.15"
  exit 1
else
  echo "$(python3 --version)"
fi

# install julearn
git clone https://github.com/juaml/julearn.git
cd julearn
git checkout julearn_sk_pandas
pip install -e .
cd .. 

I then ran the following example in julearn:

from sklearn.model_selection import GroupKFold
from sklearn.datasets import make_regression
import pandas as pd

from julearn.api import run_cross_validation
from julearn.utils import configure_logging

configure_logging(level="INFO")

# prepare data
X, y = make_regression(n_features=10, n_samples=200)
groups = sum([[i] * 40 for i in range(1, 6)], [])

data = pd.DataFrame(X)
X_names = data.columns.astype(str).to_list()
data.columns = X_names

data["target"] = y
data["groups"] = groups


# initialise cv objects
cv_outer = GroupKFold(n_splits=10)
cv_inner = GroupKFold(n_splits=5)

# configure inner cv in search params
model_params = {
    "svm__C": [1, 2],
    "search": "grid",
    "search_params": {
        "cv": cv_inner,
        "verbose": 1,
    }
}

# run
scores, final = run_cross_validation(
    X=X_names,
    y="target",
    data=data,
    model="svm",
    model_params=model_params,
    cv=cv_inner,
    seed=200,
    groups="groups",
    problem_type="regression"
)

This results in the following error:

5 fits failed with the following error:
Traceback (most recent call last):
  File "/home/lsasse/miniconda3/envs/example_venv/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/lsasse/miniconda3/envs/example_venv/lib/python3.9/site-packages/sklearn/model_selection/_search.py", line 875, in fit
    self._run_search(evaluate_candidates)
  File "/home/lsasse/miniconda3/envs/example_venv/lib/python3.9/site-packages/sklearn/model_selection/_search.py", line 1389, in _run_search
    evaluate_candidates(ParameterGrid(self.param_grid))
  File "/home/lsasse/miniconda3/envs/example_venv/lib/python3.9/site-packages/sklearn/model_selection/_search.py", line 834, in evaluate_candidates
    for (cand_idx, parameters), (split_idx, (train, test)) in product(
  File "/home/lsasse/miniconda3/envs/example_venv/lib/python3.9/site-packages/sklearn/model_selection/_split.py", line 352, in split
    for train, test in super().split(X, y, groups):
  File "/home/lsasse/miniconda3/envs/example_venv/lib/python3.9/site-packages/sklearn/model_selection/_split.py", line 85, in split
    for test_index in self._iter_test_masks(X, y, groups):
  File "/home/lsasse/miniconda3/envs/example_venv/lib/python3.9/site-packages/sklearn/model_selection/_split.py", line 97, in _iter_test_masks
    for test_index in self._iter_test_indices(X, y, groups):
  File "/home/lsasse/miniconda3/envs/example_venv/lib/python3.9/site-packages/sklearn/model_selection/_split.py", line 530, in _iter_test_indices
    raise ValueError("The 'groups' parameter should not be None.")
ValueError: The 'groups' parameter should not be None.

So my guess is that julearn does not automatically hand over the groups parameter (which makes sense since the inner cv need not necessarily have a groups parameter). Is there a way for me to add the groups parameter in the search_params?

Expected behavior
A clear and concise description of what you expected to happen.

Either I should be able to provide the groups parameter for the inner cv in the search_params, or it should be handed over automatically by julearn after some additional checks.

Screenshots
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System (please complete the following information):

• OS: [e.g. macOS / Linux / Windows]
• Version [e.g. 22]

Additional context
Add any other context about the problem here.

[LeSasse]

I did previously hard code a solution into my local installation like this in api.py for this specific purpose, by adding the fit_params, but it should likely be done better, since this only works in my specific GroupKFold context

    fit_params = {"groups": df_groups.values}
    scores = cross_validate(pipeline, df_X_conf, y, cv=cv_outer,
                            scoring=scorer, groups=df_groups,
                            return_estimator=cv_return_estimator, fit_params=fit_params)

[LeSasse]

it was pointed out to me that #141 is quite similar in nature