cluster_num, age_group_number, age_group_dict,
group_age_dict, age_list, deltaX, k_threshold, l_threshold,
df_train, df_item_age_uid, pp)
elif method == 'YGen':
X_obf_dict, X_ori = obfuscations.YGen(
df_train, age_group_number, cluster_num, age_list,
age_group_dict, group_age_dict, df_item_age_uid, deltaX,
k_threshold, l_threshold, pp)
elif method == 'XObf':
X_obf_dict, X_ori = obfuscations.XObf(deltaX, cluster_num,
age_group_number,
age_list, group_age_dict,
df_train, pp)
elif method == 'PrivCheck':
X_obf_dict, X_ori = obfuscations.PrivCheck(
deltaX, cluster_num, age_group_number, df_cluster,
df_train, age_list, age_group_dict, group_age_dict, pp)
elif method == 'DP':
X_obf_dict, X_ori = obfuscations.differential_privacy(
df_train, age_group_dict, beta)
elif method == 'Frapp':
X_obf_dict, X_ori = obfuscations.Frapp(df_train, df_test,
age_group_dict, gamma)
elif method == 'Random':
X_obf_dict, X_ori = obfuscations.Random(
df_train, age_group_dict, p_rand)
elif method == 'Sim':
X_obf_dict, X_ori = obfuscations.Similarity(
df_train, age_group_dict, pp)
else:
print('Method error. Check method setting.')
df_train = df_cluster.drop(items_test, axis=1)
df_test = df_cluster.drop(items_train, axis=1)
if method == 'HyObscure':
X_obf_dict, X_ori, model_rf, model_xgb = obfuscations.HyObscure(df_train, grid_area_dict, area_grid_dict, cluster_num, grid_area_number, grid_list,
area_reducibility, area_grid_rowcol_dict, area_grid_colrow_dict, method,
grid_rowcol, grid_colrow, l_threshold, k_threshold, deltaX, pp)
elif method == 'YGen':
X_obf_dict, X_ori, model_rf, model_xgb = obfuscations.YGen(df_train, grid_area_dict, grid_area_number, cluster_num, grid_list, area_grid_dict, deltaX,
area_reducibility, area_grid_rowcol_dict, area_grid_colrow_dict, method,
grid_rowcol, grid_colrow, l_threshold, k_threshold, pp)
elif method == 'XObf':
X_obf_dict, X_ori, model_rf, model_xgb = obfuscations.XObf(df_train, cluster_num, grid_area_number, grid_list, grid_area_dict, area_grid_dict, deltaX, pp, method)
elif method == 'PrivCheck':
X_obf_dict, X_ori, model_rf, model_xgb = obfuscations.PrivCheck(df_train, cluster_num, grid_list, grid_area_dict, grid_area_number, area_grid_dict, deltaX, pp)
elif method == 'DP':
X_obf_dict, X_ori, model_rf, model_xgb = obfuscations.differential_privacy(df_train, grid_area_dict, grid_area_number, beta)
elif method == 'Frapp':
X_obf_dict, X_ori, model_rf, model_xgb = obfuscations.Frapp(df_train, grid_area_dict, gamma)
elif method == 'Random':
X_obf_dict, X_ori, model_rf, model_xgb = obfuscations.Random(df_train, grid_area_dict, p_rand)
elif method == 'Sim':
X_obf_dict, X_ori, model_rf, model_xgb = obfuscations.Similarity(df_train, grid_area_dict, pp)
else:
print('Method error. Check method setting.')
break
df_test = funcs.update_grid_group(df_test, grid_area_dict)
rec_oris = []
print("all users num: {}".format(len(df_item_ageGroup_uid)))
print("split train and test over")
print("train num {}".format(len(df_train)))
print("test num {}".format(len(df_test)))
print("train items {}".format(df_train_items.shape[1]))
print("test items {}".format(df_test_items.shape[1]))
if method == 'HyObscure':
X_obf_dict, X_ori, model_rf, model_xgb = obfuscations.HyObscure(
df_train, df_test, df_test_rec_items, df_item_age_uid,
age_group_dict, group_age_dict, cluster_num,
age_group_number, age_list, deltaX, k_threshold,
l_threshold, pp)
elif method == 'PrivCheck':
X_obf_dict, X_ori, model_rf, model_xgb = obfuscations.PrivCheck(
df_train, df_test, df_test_rec_items, age_group_number,
cluster_num, deltaX, age_list, age_group_dict,
group_age_dict, pp)
else:
print('Method error. Check method setting.')
break
mae_oris_rf = []
mae_obfs_rf = []
mae_oris_xgb = []
mae_obfs_xgb = []
rec_oris = []
rec_obfs = []
for i in range(100):
rmse_ori, rmse_obf = recommendation(X_obf_dict[i], X_ori,
