docker build -t stat-util . docker run --rm -p 8889:8889 -v `pwd`:/workspace stat-util Code for all use cases is provided in examples.ipynb notebook.
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 )