I am working with an imbalanced data set that has multiple observations from the same set of users. I want to make sure that I don't have the same users in both the training and test sets while still maintaining the original distribution as much as possible. I have been trying to combine the GroupKFold and StratifiedKFold functions from Sklearn but I'm kind of at a loss how to do it. Does anyone have any ideas of how I could combine these two functions?
1 Answers
def stratified_group_k_fold(X, y, groups, k, seed=None):
"""Source: https://www.kaggle.com/jakubwasikowski/stratified-group-k-fold-cross-validation """
labels_num = np.max(y) + 1
y_counts_per_group = collections.defaultdict(lambda: np.zeros(labels_num))
y_distr = collections.Counter()
for label, g in zip(y, groups):
y_counts_per_group[g][label] += 1
y_distr[label] += 1
y_counts_per_fold = collections.defaultdict(lambda: np.zeros(labels_num))
groups_per_fold = collections.defaultdict(set)
def eval_y_counts_per_fold(y_counts, fold):
y_counts_per_fold[fold] += y_counts
std_per_label = []
for label in range(labels_num):
label_std = np.std([y_counts_per_fold[i][label] / y_distr[label] for i in range(k)])
std_per_label.append(label_std)
y_counts_per_fold[fold] -= y_counts
return np.mean(std_per_label)
groups_and_y_counts = list(y_counts_per_group.items())
random.Random(seed).shuffle(groups_and_y_counts)
for g, y_counts in sorted(groups_and_y_counts, key=lambda x: -np.std(x[1])):
best_fold = None
min_eval = None
for i in range(k):
fold_eval = eval_y_counts_per_fold(y_counts, i)
if min_eval is None or fold_eval < min_eval:
min_eval = fold_eval
best_fold = i
y_counts_per_fold[best_fold] += y_counts
groups_per_fold[best_fold].add(g)
all_groups = set(groups)
for i in range(k):
train_groups = all_groups - groups_per_fold[i]
test_groups = groups_per_fold[i]
train_indices = [i for i, g in enumerate(groups) if g in train_groups]
test_indices = [i for i, g in enumerate(groups) if g in test_groups]
yield train_indices, test_indices
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