DavidRosen

from tqdm import trange
DFLT_NBOOTS=500
def raw_metric_samples(metrics, *data_args, nboots=DFLT_NBOOTS, sort=False,
       **metric_kwargs):
    """Return dataframe containing metric(s) for nboots boot sample datasets.
    metrics is a metric func or iterable of funcs e.g. [m1, m2, m3]
    """
    if callable(metrics): metrics=[metrics] # single metric func to list
    metrics=list(metrics) # in case it is a generator
    dforig=pd.DataFrame\

DavidRosen

hardpredtst=gbc.predict(Xtest)
def conf_matrix(y,pred):
    ((tn, fp), (fn, tp)) = metrics.confusion_matrix(y, pred)
    ((tnr,fpr),(fnr,tpr))= metrics.confusion_matrix(y, pred, normalize='true')
    return pd.DataFrame([[f'TN = {tn} (TNR = {tnr:1.2%})', f'FP = {fp} (FPR = {fpr:1.2%})'], # 97
                         [f'FN = {fn} (FNR = {fnr:1.2%})', f'TP = {tp} (TPR = {tpr:1.2%})']],#96
            index=['True 0(Legit)', 'True 1(Fraud)'], 
            columns=['Pred 0(Approve as Legit)', 'Pred 1(Deny as Fraud)'])
conf_matrix(ytest,hardpredtst)