from statsmodels.stats.outliers_influence import variance_inflation_factor

vif_threshold = 10
all_feat = lr_model_feats
max_vif_value = np.Inf
max_vif_feature = None
iter_num = 0
while max_vif_value > vif_threshold:
    iter_num += 1
    t_start = - time.time()
    print('-' * 100)
    print(max_vif_value, max_vif_feature, len(all_feat))
    if max_vif_feature is not None:
        all_feat = [t for t in all_feat if t != max_vif_feature]
    vif = pd.DataFrame()
    X = calc_df[all_feat]
    vif["vif_value"] = [variance_inflation_factor(X.fillna(0).values, i) for i in range(X.shape[1])]
    vif['feature_name'] = X.columns
    max_vif_value = vif.vif_value.max()
    max_vif_feature = vif.sort_values("vif_value", ascending=False).head(1).feature_name.values[0]
    t_cost = time.time() + t_start
    print("---- iter %s: cost %s" % (iter_num, t_cost))

print("done!")
print(max_vif_value, max_vif_feature)
print(len(all_feat))
print(all_feat)