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summary.py
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summary.py
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import os
import pandas as pd
import numpy as np
import json
import shutil
import copy
def get_metrics(duration_list, traffic_name, total_summary_metrics, num_of_out):
# calculate the mean final 10 rounds
validation_duration_length = 5
duration_list = np.array(duration_list)
validation_duration = duration_list[-validation_duration_length:]
validation_through = num_of_out[-validation_duration_length:]
final_through = np.round(np.mean(validation_through), decimals=2)
final_duration = np.round(np.mean(validation_duration[validation_duration > 0]), decimals=2)
final_duration_std = np.round(np.std(validation_duration[validation_duration > 0]), decimals=2)
total_summary_metrics["traffic"].append(traffic_name)
total_summary_metrics["final_duration"].append(final_duration)
total_summary_metrics["final_duration_std"].append(final_duration_std)
total_summary_metrics["final_through"].append(final_through)
return total_summary_metrics
def summary_detail_RL(memo_rl, total_summary_rl):
"""
Used for test RL results
"""
records_dir = os.path.join("records", memo_rl)
for traffic_file in os.listdir(records_dir):
if ".json" not in traffic_file:
continue
print(traffic_file)
traffic_env_conf = open(os.path.join(records_dir, traffic_file, "traffic_env.conf"), 'r')
dic_traffic_env_conf = json.load(traffic_env_conf)
run_counts = dic_traffic_env_conf["RUN_COUNTS"]
num_intersection = dic_traffic_env_conf['NUM_INTERSECTIONS']
duration_each_round_list = []
num_of_vehicle_in = []
num_of_vehicle_out = []
test_round_dir = os.path.join(records_dir, traffic_file, "test_round")
try:
round_files = os.listdir(test_round_dir)
except:
print("no test round in {}".format(traffic_file))
continue
round_files = [f for f in round_files if "round" in f]
round_files.sort(key=lambda x: int(x[6:]))
for round_rl in round_files:
df_vehicle_all = []
for inter_index in range(num_intersection):
try:
round_dir = os.path.join(test_round_dir, round_rl)
df_vehicle_inter = pd.read_csv(os.path.join(round_dir, "vehicle_inter_{0}.csv".format(inter_index)),
sep=',', header=0, dtype={0: str, 1: float, 2: float},
names=["vehicle_id", "enter_time", "leave_time"])
# [leave_time_origin, leave_time, enter_time, duration]
df_vehicle_inter['leave_time_origin'] = df_vehicle_inter['leave_time']
df_vehicle_inter['leave_time'].fillna(run_counts, inplace=True)
df_vehicle_inter['duration'] = df_vehicle_inter["leave_time"].values - \
df_vehicle_inter["enter_time"].values
tmp_idx = []
for i, v in enumerate(df_vehicle_inter["vehicle_id"]):
if "shadow" in v:
tmp_idx.append(i)
df_vehicle_inter.drop(df_vehicle_inter.index[tmp_idx], inplace=True)
ave_duration = df_vehicle_inter['duration'].mean(skipna=True)
print("------------- inter_index: {0}\tave_duration: {1}".format(inter_index, ave_duration))
df_vehicle_all.append(df_vehicle_inter)
except:
print("======= Error occured during reading vehicle_inter_{}.csv")
if len(df_vehicle_all) == 0:
print("====================================EMPTY")
continue
df_vehicle_all = pd.concat(df_vehicle_all)
# calculate the duration through the entire network
vehicle_duration = df_vehicle_all.groupby(by=['vehicle_id'])['duration'].sum()
ave_duration = vehicle_duration.mean() # mean amomng all the vehicle
duration_each_round_list.append(ave_duration)
num_of_vehicle_in.append(len(df_vehicle_all['vehicle_id'].unique()))
num_of_vehicle_out.append(len(df_vehicle_all.dropna()['vehicle_id'].unique()))
print("==== round: {0}\tave_duration: {1}\tnum_of_vehicle_in:{2}\tnum_of_vehicle_out:{2}"
.format(round_rl, ave_duration, num_of_vehicle_in[-1], num_of_vehicle_out[-1]))
duration_flow = vehicle_duration.reset_index()
duration_flow['direction'] = duration_flow['vehicle_id'].apply(lambda x: x.split('_')[1])
duration_flow_ave = duration_flow.groupby(by=['direction'])['duration'].mean()
print(duration_flow_ave)
result_dir = os.path.join("summary", memo_rl, traffic_file)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
_res = {
"duration": duration_each_round_list,
"vehicle_in": num_of_vehicle_in,
"vehicle_out": num_of_vehicle_out
}
result = pd.DataFrame(_res)
result.to_csv(os.path.join(result_dir, "test_results.csv"))
total_summary_rl = get_metrics(duration_each_round_list, traffic_file, total_summary_rl, num_of_vehicle_out)
total_result = pd.DataFrame(total_summary_rl)
total_result.to_csv(os.path.join("summary", memo_rl, "total_test_results.csv"))
def summary_detail_conventional(memo_cv):
"""
Used for test conventional results.
"""
total_summary_cv = []
records_dir = os.path.join("records", memo_cv)
for traffic_file in os.listdir(records_dir):
if "anon" not in traffic_file:
continue
traffic_conf = open(os.path.join(records_dir, traffic_file, "traffic_env.conf"), 'r')
dic_traffic_env_conf = json.load(traffic_conf)
run_counts = dic_traffic_env_conf["RUN_COUNTS"]
print(traffic_file)
train_dir = os.path.join(records_dir, traffic_file)
use_all = True
if use_all:
with open(os.path.join(records_dir, traffic_file, 'agent.conf'), 'r') as agent_conf:
dic_agent_conf = json.load(agent_conf)
df_vehicle_all = []
NUM_OF_INTERSECTIONS = int(traffic_file.split('_')[1]) * int(traffic_file.split('_')[2])
for inter_id in range(int(NUM_OF_INTERSECTIONS)):
vehicle_csv = "vehicle_inter_{0}.csv".format(inter_id)
df_vehicle_inter_0 = pd.read_csv(os.path.join(train_dir, vehicle_csv),
sep=',', header=0, dtype={0: str, 1: float, 2: float},
names=["vehicle_id", "enter_time", "leave_time"])
# [leave_time_origin, leave_time, enter_time, duration]
df_vehicle_inter_0['leave_time_origin'] = df_vehicle_inter_0['leave_time']
df_vehicle_inter_0['leave_time'].fillna(run_counts, inplace=True)
df_vehicle_inter_0['duration'] = df_vehicle_inter_0["leave_time"].values - df_vehicle_inter_0[
"enter_time"].values
tmp_idx = []
for i, v in enumerate(df_vehicle_inter_0["vehicle_id"]):
if "shadow" in v:
tmp_idx.append(i)
df_vehicle_inter_0.drop(df_vehicle_inter_0.index[tmp_idx], inplace=True)
ave_duration = df_vehicle_inter_0['duration'].mean(skipna=True)
print("------------- inter_index: {0}\tave_duration: {1}".format(inter_id, ave_duration))
df_vehicle_all.append(df_vehicle_inter_0)
df_vehicle_all = pd.concat(df_vehicle_all, axis=0)
vehicle_duration = df_vehicle_all.groupby(by=['vehicle_id'])['duration'].sum()
ave_duration = vehicle_duration.mean()
num_of_vehicle_in = len(df_vehicle_all['vehicle_id'].unique())
num_of_vehicle_out = len(df_vehicle_all.dropna()['vehicle_id'].unique())
save_path = os.path.join('records', memo_cv, traffic_file).replace("records", "summary")
if not os.path.exists(save_path):
os.makedirs(save_path)
# duration.to_csv(os.path.join(save_path, 'flow.csv'))
total_summary_cv.append(
[traffic_file, ave_duration, num_of_vehicle_in, num_of_vehicle_out, dic_agent_conf["FIXED_TIME"]])
else:
shutil.rmtree(train_dir)
total_summary_cv = pd.DataFrame(total_summary_cv)
total_summary_cv.sort_values([0], ascending=[True], inplace=True)
total_summary_cv.columns = ['TRAFFIC', 'DURATION', 'CAR_NUMBER_in', 'CAR_NUMBER_out', 'CONFIG']
total_summary_cv.to_csv(os.path.join("records", memo_cv,
"total_baseline_results.csv").replace("records", "summary"),
sep='\t', index=False)
if __name__ == "__main__":
"""Only use these data"""
total_summary = {
"traffic": [],
"final_duration": [],
"final_duration_std": [],
"final_through": [],
}
memo = "benchmark_0216_t_13"
summary_detail_RL(memo, copy.deepcopy(total_summary))
# summary_detail_conventional(memo)