cross validating model with multiple input/output

expr.py

def multi_array_shuffle(*arrays, random_state=1):
    array_length = arrays[0].shape[0]
    permutated = []
    np.random.seed(random_state)
    permutation = np.random.permutation(array_length)
    for array in arrays:
        permutated.append(array[permutation, ...])
    return permutated

def cv_model_func(model_func, inputs, targets, scores_func, label=None, seed=1, fold=5, **kwargs):
    """
    cross validating model with multiple inputs /multiple output
    :param seed: seed of the split
    :param label: label used to do straitify
    :param scores_func: scores function to report
    :param targets: list of target array
    :param inputs:  list of feature matrix
    :param fold:  fold of cross validating if set to 1, do train test split
    :param model_func:  model to cross validate with a fit() and a predict() method
    :return: list of performances
    """
    num_sample = targets[0].shape[0]
    inputs = multi_array_shuffle(*inputs, random_state=seed)
    targets = multi_array_shuffle(*targets, random_state=seed)
    if label is None:
        kfold = KFold(n_splits=fold, random_state=seed, shuffle=False)
    elif callable(label):
        label = label(targets)
        kfold = StratifiedKFold(n_splits=fold, random_state=seed, shuffle=False)
    else:
        kfold = StratifiedKFold(n_splits=fold, random_state=seed, shuffle=False)

    holder = np.zeros([num_sample, 1])
    scores = []

    for train_index, test_index in kfold.split(holder, label):
        input_tr = []
        input_te = []
        target_tr = []
        target_te = []
        for feature in inputs:
            input_tr.append(feature[train_index, ...])
            input_te.append(feature[test_index, ...])
        for target in targets:
            target_tr.append(target[train_index, ...])
            target_te.append(target[test_index, ...])
        model = model_func(input_tr, target_tr, **kwargs)

        if 'predict_param' in kwargs:
            predictions = model.predict(input_te, **kwargs['predict_param'])
        else:
            predictions = model.predict(input_te)
        model = None
        scores.append(scores_func(target_te, predictions))
    return np.array(scores)