def plot_EM_results():
# 绘制测试结果图像
from param_options import args_parser
from plot_indexes_resluts import plot_indicator_results
from util_tools import mkdir
args = args_parser()
args.dataset_path = './constructed_datasets_6_30(1)/'
exp_name = 'EM'
result_save_file = './results/' + exp_name + '/'
plots_file = './plot_results/' + exp_name + '/'
print(
'====================== Save every component indicator result ========================'
)
for indicator in ['rmse', 'd2', 'r2']:
print('{} results'.format(indicator))
fig_save_path = plots_file + indicator + '/'
mkdir(fig_save_path)
for component in args.select_dim:
results_logdir = [result_save_file + indicator + '/']
plot_indicator_results(results_logdir,
fig_save_path,
component,
xaxis='station',
leg=['EM'])
print(">>> Finished save resluts figures!")
def plot_datasets():
# a = np.array([[1, 2, 3], [6, 9, 7]])
# b = np.array([[7, 4, 6], [1, 6, 7]])
# c = np.array([[6, 7, 1], [6, 2, 5]])
# l = [a, b, c]
# d = np.array(l)
# print('d: ', d)
from param_options import args_parser
from plot_indexes_resluts import plot_all_datasets
from loadDatasets import get_saved_datasets
args = args_parser()
args.dataset_path = './constructed_datasets_6_dn(10_SI2)/'
all_station_datasets = get_saved_datasets(args)
plot_all_datasets(all_station_datasets, args.select_dim,
args.selected_stations, args.dataset_path)
def plot_all_rmse_test():
from param_options import args_parser
from plot_indexes_resluts import plot_indicator_results
args = args_parser()
# 做实验
exp_time = 5
exp_name = 'FedWeightAvg(load_numbers_soft)_(5-1)_dn(10)_100_32(lrS_Adam_Fed_0.001_100_0.001_100_bs_128)_32'
result_save_file = './results/' + exp_name + '/'
indicator = 'all_rmse'
# 绘制测试结果图像
print(
'====================== Save every component indicator result ========================'
)
results_logdir = [
result_save_file + indicator + '/' + model_name + '/'
for model_name in ['fed', 'idpt']
]
fig_save_path = result_save_file + indicator + '/'
plot_indicator_results(results_logdir, fig_save_path, 'all_rmse')
def plot_federated_indicator_avg_results(dataset_number):
args = args_parser()
# dataset_number = 'one_mi_v1((A5)_1)'
results_saved_file = 'results_one_dn'
results_plot_file = 'plot_results_one_dn'
exp_name = '{}_latest'.format(dataset_number, )
result_save_root = './{}/'.format(results_saved_file) + exp_name + '/'
plots_save_root = './{}/'.format(results_plot_file) + exp_name + '/'
indicator_name = 'all_rmse'
leg = ['Fed', 'Independent']
results_logdir = [
result_save_root + 'avg_' + indicator_name + '/' + model_name + '/'
for model_name in leg
]
fig_save_path = plots_save_root + indicator_name + '/'
csv_save_fpth = result_save_root + 'avg_' + indicator_name + '/'
mkdir(fig_save_path)
# 将每个数据集计算的均值再计算总的均值和绘制方差均值线图
plot_indicator_avg_results_m(results_logdir, fig_save_path, 'station',
indicator_name, csv_save_fpth)
test_x=testX,
train_m=trainM,
test_m=testM,
min_val=Min_val,
max_val=Max_val,
train_no=Train_No,
test_no=Test_No)
all_data_list.append(dataset)
return all_data_list
if __name__ == '__main__':
from param_options import args_parser
args = args_parser()
exp_num = 5
generate_condition = 'one_time' # general / one_time / missing_rate
dataset_name_temp = '(A5_A10_A15)_nCO_321r'
params_test_list = []
test_param_name = None
if generate_condition == 'missing_rate':
params_test_list = [5, 10, 15, 20, 25, 30, 35, 40]
test_param_name = 'missing_rate'
Dname_prefix = '(A{})'
Dname_suffix = 'norm_1r_1P'
elif generate_condition == 'one_time':
params_test_list = [1]
plot_indicator_avg_results_m(results_logdir,
fig_save_path,
'station',
indicator_name,
csv_save_fpth,
select_dim=arg.select_dim)
# plot_fed_avg_acc(results_logdir, indicator_name, fig_save_path)
# plot_indicator_results(results_logdir, fig_save_path, indicator_name)
print(
"\033[0;34;40m >>>[Visulize] Finished save {} resluts figures! \033[0m"
.format(indicator_name))
if __name__ == '__main__':
arg = args_parser()
# 做实验
exp_total_time = 1
cross_validation_set_num = 5
result_saved_file = 'Fed_wGAN_results'
result_plot_file = 'Fed_wGAN_plot_results_test'
indicator_list = ['rmse', 'd2', 'r2', 'all_rmse']
# params_test_list = [0.9]
# test_param_name = 'p_hint'
# 训练模式，是训练一次还是根据不同的参数训练多次
training_model = 'One_time' # Many_time / One_time
def run_multi_cross_validation_datasets_test():
"""
用于计算每个数据集的学习训练测试结果的总学习测试结果的平均
:return:
"""
args = args_parser()
# 做实验
exp_total_time = 10
cross_validation_sets = 5
exp_name = 'FedWeightAvg(soft)_6_dn(15)_100_32(0.001_0.001_bs_128)_32(phint_0.95)'
indicator_list = ['rmse', 'd2', 'r2', 'all_rmse']
# for i in range(cross_validation_sets):
# print('============= Start training at datasets {} =============='.format(i))
# # 用于统计各种指标，建立相对应的文件夹
# result_save_file = './results_v2/' + exp_name + '/datasets_{}/'.format(i)
# plots_file = './plot_results/' + exp_name + '/datasets_{}/'.format(i)
# for exp_t in range(exp_total_time):
# # 当前数据集
# args.dataset_path = './constructed_datasets_6_dn(5)/{}/'.format(i)
# fed_gain_test_exp(args, result_save_file, i)
# print('>>> Finished training & testing!')
result_save_root = './results_v3/' + exp_name + '/'
plots_save_root = './plot_results/' + exp_name + '/'
indicator_name = 'all_rmse'
leg = ['Fed', 'Independent']
'''
# 建立保存结果的文件夹
indicator_results_csv_save_fpth = result_save_root + indicator_name + '/'
for mode in leg:
mkdir(indicator_results_csv_save_fpth+mode+'/')
# 计算每个数据集的几次实验的均值
for c in range(cross_validation_sets):
results_logdir = [result_save_root + 'datasets_' + str(c) + '/' + indicator_name + '/' + model_name + '/'
for model_name in ['fed', 'idpt']]
compute_avg_of_data_in_file(args, c, results_logdir, indicator_results_csv_save_fpth,
indicator_name)
'''
# 绘制测试结果图像
print(
'====================== Save every component indicator result ========================'
)
print('{} results'.format(indicator_name))
results_logdir = [
result_save_root + indicator_name + '/' + model_name + '/'
for model_name in leg
]
fig_save_path = plots_save_root + indicator_name + '/'
csv_save_fpth = result_save_root + indicator_name + '/'
mkdir(fig_save_path)
# 计算几次实验的最后的平均值并画出均值和方差曲线图
plot_indicator_avg_results_m(results_logdir, fig_save_path, 'station',
indicator_name, csv_save_fpth)
# plot_indicator_results(results_logdir, fig_save_path, indicator_name)
print(">>> Finished save resluts figures!")