@@ -0,0 +1,932 @@
+import numpy as np%0Aimport sklearn.utils as skutils%0Afrom mimic3models import metrics%0Aimport argparse%0A%0A%0Adef main():%0A    parser = argparse.ArgumentParser()%0A    parser.add_argument('filename', type=str)%0A    args = parser.parse_args()%0A%0A    data = np.loadtxt(args.filename, delimiter=',', skiprows=1)%0A    n_iters = 1000%0A    aucs = %5B%5D%0A    for i in range(n_iters):%0A        cur_data = skutils.resample(data, n_samples=len(data))%0A        pred = cur_data%5B:, 0%5D%0A        y_true = cur_data%5B:, 1%5D%0A        ret = metrics.print_metrics_binary(y_true, pred, verbose=0)%5B%22auroc%22%5D%0A        aucs += %5Bret%5D%0A%0A    print %22%7B%7D iterations%22.format(n_iters)%0A    print %22mean = %7B%7D%22.format(np.mean(aucs))%0A    print %22median = %7B%7D%22.format(np.median(aucs))%0A    print %22std = %7B%7D%22.format(np.std(aucs))%0A    print %222.5%25 percentile = %7B%7D%22.format(np.percentile(aucs, 2.5))%0A    print %2297.5%25 percentile = %7B%7D%22.format(np.percentile(aucs, 97.5))%0A%0Aif __name__ == %22__main__%22:%0A    main()%0A%0A

add evaluation script for decomp