from sklearn.metrics import roc_auc_score

import stat_util


score, ci_lower, ci_upper, scores = stat_util.score_ci(
    y_true, y_pred, score_fun=roc_auc_score
)

from sklearn.metrics import roc_auc_score

import stat_util


p, z = stat_util.pvalue(y_true, y_pred1, y_pred2, score_fun=roc_auc_score)

import numpy as np
from sklearn.metrics import roc_auc_score

import stat_util


mean_score, ci_lower, ci_upper, scores = stat_util.score_stat_ci(
    y_true, y_pred_readers, score_fun=roc_auc_score, stat_fun=np.mean
)

import numpy as np
from sklearn.metrics import roc_auc_score

import stat_util


p, z = stat_util.pvalue_stat(
    y_true, y_pred, y_pred_readers, score_fun=roc_auc_score, stat_fun=np.mean
)