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plot_results_fig34.py
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import argparse
import os
import numpy as np
import matplotlib.pyplot as plt
NUM_BINS = 100
BITS_IN_BYTE = 8.0
MILLISEC_IN_SEC = 1000.0
M_IN_B = 1000000.0
VIDEO_LEN = 48
VIDEO_BIT_RATE = [300, 750, 1200, 1850, 2850, 4300]
K_IN_M = 1000.0
REBUF_P = 4.3
SMOOTH_P = 1
COLOR_MAP = plt.cm.rainbow #nipy_spectral, Set1,Paired sequential: 'viridis' cubehelix, jet, rainbow, RdBu, tab20b
SIM_DP = 'sim_dp'
PROPOSED_SCHEME = 'sim_bayesmpc'
PROPOSED_SCHEME_NAME = 'BayesMPC'
#SCHEMES = ['BB', 'RB', 'FIXED', 'FESTIVE', 'BOLA', 'RL', 'sim_rl', SIM_DP
SCHEMES = ['sim_bayesmpc', 'sim_bb', 'sim_rl', 'sim_rb', 'sim_mpc', 'sim_bola'] #, 'sim_pnn3f_mdmpc', 'sim_mpc', 'sim_future7_mpc' , 'sim_pnn3f_mdmpc' 'sim_std3bbpmpc', 'sim_bbpmpc', 'sim_rb', , 'sim_future4_mpc'
SCHEMES_NAME = ['BayesMPC', 'Buffer-based', 'Pensieve', 'Rate-based', 'RobustMPC', 'BOLA'] # , 'cnnMPC', 'OurProposed-PNN', 'RB', , 'Offline-Optimal'
# SCHEMES = ['sim_bayesmpc', 'sim_mpc', 'sim_10bayesmpc', 'sim_13bayesmpc', 'sim_t1bayesmpc', 'sim_17bayesmpc', 'sim_no_bayesmpc', 'sim_CNNmpc'] #, 'sim_mpc', 'sim_future7_mpc' , 'sim_pnn3f_mdmpc' 'sim_std3bbpmpc', 'sim_bbpmpc',
# SCHEMES_NAME = ['BayesMPC', 'Robust', 'Bayes(z=1.0)', 'Bayes (z= 1.3)', 'Bayes(z=1.5)', 'Bayes(z=1.7)', 'Bayes(PE)', 'CNN-based'] # , 'OurProposed-PNN'
# SCHEMES = ['sim_bayesmpc', 'sim_t1bayesmpc', 'sim_10bayesmpc', 'sim_PEbayesmpc'] #
# SCHEMES_NAME = ['BayesMPC', 'Bayes(z=1.0)', 'Bayes(z=1.5)', 'Bayes(PE)'] # , 'OurProposed-PNN'
COLOR_CDF = ['r', '#800080', 'c', '', '', '']
LINE_STY = ['-', ':', '--', '-.', ':', '-.', '-.', '-'] # style set of the lines of figure
HATCH = ['', '/', '+', '\\', '//', '-', 'x']
parser = argparse.ArgumentParser(description='Plot_figure34')
parser.add_argument('--lin', action='store_true', help='QoE_lin metric')
parser.add_argument('--log', action='store_true', help='QoE_log metric')
parser.add_argument('--FCC', action='store_true', help='Test in FCC dataset')
parser.add_argument('--HSDPA', action='store_true', help='Test in HSDPA dataset')
parser.add_argument('--Oboe', action='store_true', help='Test in Oboe dataset')
def Plot_Bar(Plot_value, Y_Label, X_Label, Legend, Legend_column, intra_width = 0.1, intra_interval = 0, inter_width = 0.2, Y_bottom = 0, X_right = 0): # plot a bar, the Plot_value should be a dictionary, the 'Y_Lable' should be a string, the 'X_Label' and 'Legend' should be both a list of strings, 'Legend_column' is the column numbers of the legend, 'intra_width' is the width of a bar on the x-axis, 'intra_interval' is the width of interval of bars on the x-axis in a same term, 'inter_width' is the distance of different terms on the x-axis, 'Y_bottom' is the start value of y-axis
fig = plt.figure()
ax = fig.add_subplot(111)
# left_start_pos = 0.1
# intra_width = 0.08
# inter_width = 0.12
x_label_pos = []
colors = [COLOR_MAP(i) for i in np.linspace(0, 1, len(Legend))]
compare_term_num = len(X_Label) # the number of terms that should be compared
pos = intra_width * 3 / float(2) # start position
for term in range(compare_term_num):
left_start_pos = pos - intra_width/float(2)
for method in range(len(Legend)):
# pdb.set_trace()
plt.bar(pos, Plot_value[term][method], width = intra_width, alpha = 0.5, color = colors[method], edgecolor = 'k', hatch = HATCH[method]) #, hatch = HATCH[method])
pos += intra_width + intra_interval
x_label_pos.append(pos - intra_width/float(2) - intra_interval - (pos - intra_width/float(2) - left_start_pos) / float(2))
pos += inter_width
if Legend[0] != 'None':
le = ax.legend(Legend, loc = 1, ncol = Legend_column)
frame = le.get_frame()
frame.set_alpha(0)
frame.set_facecolor('none')
plt.ylabel(Y_Label)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.grid(axis='y', linestyle = '--')
plt.ylim(bottom = Y_bottom)
ax.set_xticks(x_label_pos)
ax.set_xticklabels(X_Label)
# plt.xticks(fontsize = 16)
# plt.yticks(fontsize = 16)
if not X_right == 0:
ax.set_xlim(left = 0, right = X_right)
plt.show()
def main():
args = parser.parse_args()
if args.lin:
qoe_metric = 'results_lin'
qoe_name = '$QoE_{lin}$'
elif args.log:
qoe_metric = 'results_log'
qoe_name = '$QoE_{log}$'
else:
print('Please select the QoE Metric!')
if args.FCC:
dataset = 'fcc'
elif args.HSDPA:
dataset = 'HSDPA'
elif args.Oboe:
dataset = 'Oboe'
else:
print('Please select the dataset!')
results_folder = './' + qoe_metric + '/' + dataset + '/'
time_all = {}
bit_rate_all = {}
quality_all = {}
buff_all = {}
rebuff_all = {}
bw_all = {}
raw_reward_all = {}
# bw_pre_all = {}
# bw_robust_all = {}
# bw_true_all = {}
bw_record = []
for scheme in SCHEMES:
time_all[scheme] = {}
raw_reward_all[scheme] = {}
bit_rate_all[scheme] = {}
quality_all[scheme] = {}
buff_all[scheme] = {}
rebuff_all[scheme] = {}
bw_all[scheme] = {}
# bw_pre_all[scheme] = {}
# bw_robust_all[scheme] = {}
# bw_true_all[scheme] = {}
log_files = os.listdir(results_folder)
for log_file in log_files:
time_ms = []
bit_rate = []
quality = []
buff = []
rebuff = []
bw = []
reward = []
# bw_pre = []
# bw_robust = []
# bw_true = []
print(log_file)
with open(results_folder + log_file, 'rb') as f:
if SIM_DP in log_file:
last_t = 0
last_b = 0
last_q = 1
lines = []
for line in f:
lines.append(line)
parse = line.split()
if len(parse) >= 6:
time_ms.append(float(parse[3]))
bit_rate.append(VIDEO_BIT_RATE[int(parse[6])])
buff.append(float(parse[4]))
bw.append(float(parse[5]))
for line in reversed(lines):
parse = line.split()
r = 0
if len(parse) > 1:
if int(parse[0]) < 48:
t = float(parse[3])
b = float(parse[4])
q = int(parse[6])
if b == 4:
rebuff = (t - last_t) - last_b
assert rebuff >= -1e-4
r -= REBUF_P * rebuff
r += VIDEO_BIT_RATE[q] / K_IN_M
r -= SMOOTH_P * np.abs(VIDEO_BIT_RATE[q] - VIDEO_BIT_RATE[last_q]) / K_IN_M
reward.append(r)
last_t = t
last_b = b
last_q = q
else:
for line in f:
parse = line.split()
if len(parse) <= 1:
break
time_ms.append(float(parse[0]))
bit_rate.append(int(parse[1]))
quality.append(float(parse[1])/1000.)
# quality.append(float(np.log(float(parse[1]) / float(VIDEO_BIT_RATE[0])))
buff.append(float(parse[2]))
rebuff.append(float(parse[3]))
bw.append(float(parse[4]) / float(parse[5]) * BITS_IN_BYTE * MILLISEC_IN_SEC / M_IN_B)
reward.append(float(parse[6]))
# bw_pre.append(float(parse[7]))
# bw_robust.append(float(parse[9]))
# bw_true.append(float(parse[10]))
if SIM_DP in log_file:
time_ms = time_ms[::-1]
bit_rate = bit_rate[::-1]
buff = buff[::-1]
bw = bw[::-1]
time_ms = np.array(time_ms)
time_ms -= time_ms[0]
# print log_file
if PROPOSED_SCHEME in log_file:
bw_record = bw_record + bw
for scheme in SCHEMES:
if scheme in log_file:
time_all[scheme][log_file[len('log_' + str(scheme) + '_'):]] = time_ms
bit_rate_all[scheme][log_file[len('log_' + str(scheme) + '_'):]] = bit_rate
quality_all[scheme][log_file[len('log_' + str(scheme) + '_'):]] = quality
buff_all[scheme][log_file[len('log_' + str(scheme) + '_'):]] = buff
rebuff_all[scheme][log_file[len('log_' + str(scheme) + '_'):]] = rebuff
bw_all[scheme][log_file[len('log_' + str(scheme) + '_'):]] = bw
raw_reward_all[scheme][log_file[len('log_' + str(scheme) + '_'):]] = reward
# bw_pre_all[scheme][log_file[len('log_' + str(scheme) + '_'):]] = bw_pre
# bw_robust_all[scheme][log_file[len('log_' + str(scheme) + '_'):]] = bw_robust
# bw_true_all[scheme][log_file[len('log_' + str(scheme) + '_'):]] = bw_true
break
# ---- ---- ---- ----bayesmpc
# Reward records
# ---- ---- ---- ----
log_file_all = []
reward_all = {}
quality_Mtrace = {}
rebuff_Mtrace = {}
smooth_Mtrace = {}
# mse_bwP_Mtrace = {}
# er_bwP_Mtraces = {}
reward_improvement = {}
for scheme in SCHEMES:
reward_all[scheme] = []
quality_Mtrace[scheme] = []
rebuff_Mtrace[scheme] = []
smooth_Mtrace[scheme] = []
# mse_bwP_Mtrace[scheme] = []
# er_bwP_Mtraces[scheme] = []
if scheme != PROPOSED_SCHEME:
reward_improvement[scheme]=[]
# BW_means = []
# BW_vars = []
for l in time_all[SCHEMES[0]]:
schemes_check = True
for scheme in SCHEMES:
if l not in time_all[scheme] or len(time_all[scheme][l]) < VIDEO_LEN:
schemes_check = False
break
if schemes_check:
log_file_all.append(l)
for scheme in SCHEMES:
reward_all[scheme].append(np.mean(raw_reward_all[scheme][l][10:VIDEO_LEN]))
# if np.mean(raw_reward_all[scheme][l][10:VIDEO_LEN]) < -3:
# print(l)
quality_Mtrace[scheme].append(np.mean(quality_all[scheme][l][10:VIDEO_LEN]))
rebuff_Mtrace[scheme].append(np.mean(rebuff_all[scheme][l][10:VIDEO_LEN]))
#calculate the quality smoothness
quality_trace = quality_all[scheme][l][10:VIDEO_LEN]
quality_last_trace = np.roll(quality_trace, 1)
quality_last_trace[0] = quality_trace[9] # gurantee the first chunk's smooth penality
trace_smoothness = [np.abs(quality_trace[ptr] - quality_last_trace[ptr]) for ptr in range(len(quality_trace))]
smooth_Mtrace[scheme].append(np.mean(trace_smoothness))
# bw_pre_trace = bw_pre_all[scheme][l][10:VIDEO_LEN]
# bw_true_trace = bw_true_all[scheme][l][10:VIDEO_LEN]
# trace_bwmse = [(bw_pre_trace[ptr] - bw_true_trace[ptr]) for ptr in range(len(bw_pre_trace))]
# mse_bwP_Mtrace[scheme].append(np.mean(trace_bwmse))
# bw_robust_trace = bw_robust_all[scheme][l][10:VIDEO_LEN]
# trace_eratio = [int(bw_robust_trace[ptr]/bw_true_trace[ptr]) for ptr in range(len(bw_robust_trace))] # 1 if bw_robust is greater than true, else 0
# er_bwP_Mtraces[scheme].append(np.mean(trace_eratio)) ## record the error ratio
# if scheme is PROPOSED_SCHEME:
# BW_means.append(np.mean(bw_all[scheme][l][10:VIDEO_LEN]))
# BW_vars.append(np.var(bw_all[scheme][l][10:VIDEO_LEN]))
BW_mean_value = np.mean(bw_record)
BW_var_value = np.var(bw_record)
for l in range(len(reward_all[PROPOSED_SCHEME])):
comparison_schemes = [SCHEMES[i] for i in range(len(SCHEMES))]
comparison_schemes.remove(PROPOSED_SCHEME)
for scheme in comparison_schemes:
reward_improvement[scheme].append(float((reward_all[PROPOSED_SCHEME][l] - reward_all[scheme][l])/1.0 )) # abs(reward_all[scheme][l])
# for l in time_all[SCHEMES[0]]:
# if np.sum(raw_reward_all['sim_mpc'][l][1:VIDEO_LEN]) != np.sum(raw_reward_all['sim_tmpc'][l][1:VIDEO_LEN]):
# print(str(l))
mean_rewards = {}
# mean_bw_mse = {}
# mean_bw_er = {}
mean_quality = []
mean_rebuffer = []
mean_smoothness = []
for scheme in SCHEMES:
# reward_norm = [float(reward_all[scheme][session] / reward_all[PROPOSED_SCHEME][session]) for session in range(len(reward_all[PROPOSED_SCHEME]))]
mean_rewards[scheme] = []
mean_rewards[scheme].append(np.mean(reward_all[scheme]))
# mean_rewards[scheme].append(np.std(reward_norm))
# mean_bw_mse[scheme] = np.mean(mse_bwP_Mtrace[scheme])
# mean_bw_er[scheme] = np.mean(er_bwP_Mtraces[scheme])
mean_quality.append(np.mean(quality_Mtrace[scheme]))
mean_rebuffer.append(np.mean(rebuff_Mtrace[scheme]) * REBUF_P)
mean_smoothness.append(np.mean(smooth_Mtrace[scheme]) * SMOOTH_P)
# ## calculate the empirical risk
# empiric_risk = {}
# near_optimal = reward_all['sim_future5_mpc']
# for scheme in SCHEMES:
# square_error = 0
# for index in range(len(near_optimal)):
# square_error += (near_optimal[index] - reward_all[scheme][index])**2
# empiric_risk[scheme] = square_error
# empiric_risk[scheme] = square_error
fig = plt.figure()
ax = fig.add_subplot(111)
for scheme in SCHEMES:
ax.plot(reward_all[scheme])
SCHEMES_REW = []
index = 0
for scheme in SCHEMES:
SCHEMES_REW.append(SCHEMES_NAME[index] + ': ' + str('%.3f' % mean_rewards[scheme][0]))
# SCHEMES_REW.append(SCHEMES_NAME[index])
index += 1
colors = [COLOR_MAP(i) for i in np.linspace(0, 1, len(ax.lines))]
for i,j in enumerate(ax.lines):
j.set_color(colors[i])
ax.legend(SCHEMES_REW, loc='best')
plt.ylabel('total reward')
plt.xlabel('trace index')
ax.spines['bottom'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
plt.grid()
plt.show()
# ---- ---- ---- ----
# CDF
# ---- ---- ---- ----
fig = plt.figure()
ax = fig.add_subplot(111)
# for method in METHODS:
# values, base = np.histogram(Method_total_qoe_trace[method], bins = NUM_BINS)
# cumulative = np.cumsum(values) / float(len(Method_total_qoe_trace[method]))
# cdf_m.plot(base[:-1], cumulative)
# colors = [COLOR_MAP(i) for i in np.linspace(0, 1, len(METHODS))]
# for i,j in enumerate(cdf_m.lines):
# # j.set_color(colors[i])
# plt.setp(j, color = colors[i], linestyle = LINE_STY[i], linewidth = 2.0) #, marker = HATCH[i]
# cdf_m.legend(METHODS_LABEL, loc = 'best', fontsize = 16)
# plt.ylabel('CDF', fontsize = 16)
# plt.xlabel("Average Values of Chunk's QoE", fontsize = 16)
# plt.xticks(fontsize = 14)
# plt.yticks(fontsize = 14)
# plt.xlim([0.930,0.994])
# plt.title('BC')
# plt.grid()
# plt.show()
for scheme in SCHEMES:
values, base = np.histogram(reward_all[scheme], bins=NUM_BINS)
cumulative = np.cumsum(values)/float(len(reward_all[scheme]))
ax.plot(base[:-1], cumulative)
colors = [COLOR_MAP(i) for i in np.linspace(0, 1, len(ax.lines))]
for i,j in enumerate(ax.lines):
# j.set_color(colors[i])
plt.setp(j, color = colors[i], linestyle = LINE_STY[i], linewidth = 2.6) #, marker = HATCH[i]
SCHEMES_REW = []
index = 0
for scheme in SCHEMES:
# SCHEMES_REW.append(SCHEMES_NAME[index] + ': ' + str('%.3f' % mean_rewards[scheme][0]))
SCHEMES_REW.append(SCHEMES_NAME[index])
index += 1
legend = ax.legend(SCHEMES_REW, loc='best', fontsize = 16)
frame = legend.get_frame()
frame.set_alpha(0)
frame.set_facecolor('none')
plt.ylabel('CDF (Perc. of sessions)', fontsize = 20)
plt.xlabel("Average Values of Chunk's " + qoe_name, fontsize = 18)
plt.xticks(fontsize = 16)
plt.yticks(fontsize = 16)
# plt.xlim([0.930,0.994])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
plt.title(dataset) # HSDPA , FCC , Oboe
# plt.grid()
plt.show()
#################################################################
# QoE reward_improvement
#################################################################
# fig = plt.figure()
# ax = fig.add_subplot(111)
# for scheme in comparison_schemes:
# values, base = np.histogram(reward_improvement[scheme], bins=NUM_BINS)
# cumulative = np.cumsum(values)/float(len(reward_improvement[scheme]))
# ax.plot(base[:-1], cumulative)
# # colors = [COLOR_MAP(i) for i in np.linspace(0, 1, len(ax.lines))]
# for i,j in enumerate(ax.lines):
# # j.set_color(colors[i])
# plt.setp(j, color = colors[i+1], linestyle = LINE_STY[i], linewidth = 2.6) #, marker = HATCH[i]
# comparison_schemes_names = [SCHEMES_NAME[i] for i in range(len(SCHEMES_NAME))]
# comparison_schemes_names.remove(PROPOSED_SCHEME_NAME)
# legend = ax.legend(comparison_schemes_names, loc='best', fontsize = 17)
# # legend = ax.legend(SCHEMES_REW, loc=4, fontsize = 14)
# frame = legend.get_frame()
# frame.set_alpha(0)
# frame.set_facecolor('none')
# plt.ylabel('CDF (Perc. of sessions)', fontsize = 20)
# plt.xlabel("$QoE_{log}$ improvement", fontsize = 18)
# plt.xticks(fontsize = 16)
# plt.yticks(fontsize = 16)
# plt.ylim([0.0,1.0])
# # plt.xlim(-0.2, 1)
# plt.vlines(0, 0, 1, colors=colors[0],linestyles='solid')
# ax.spines['top'].set_visible(False)
# ax.spines['right'].set_visible(False)
# ax.spines['bottom'].set_linewidth(2.5)
# ax.spines['left'].set_linewidth(2.5)
# plt.title('Oboe') # HSDPA , FCC , Oboe
# # plt.grid()
# plt.show()
################################################################
# QoE individual term
################################################################
Plot_Bar_value = {}
Plot_Bar_value[0] = [np.log(1+i) for i in mean_quality]
Plot_Bar_value[1] = [np.log(1+i) for i in mean_rebuffer]
Plot_Bar_value[2] = [np.log(1+i) for i in mean_smoothness]
Plot_Bar(Plot_Bar_value, "Average value", ['Bitrate utility', 'Rebuffering penalty', 'Smoothness penalty'], SCHEMES_NAME, 2, 0.2, 0, 0.2, X_right = 4.4)
# ---- ---- ---- ----
# check each trace
# ---- ---- ---- ----
# for l in time_all[SCHEMES[0]]:
# schemes_check = True
# for scheme in SCHEMES:
# if l not in time_all[scheme] or len(time_all[scheme][l]) < VIDEO_LEN:
# schemes_check = False
# break
# if schemes_check:
# fig = plt.figure()
# ax = fig.add_subplot(111)
# for scheme in SCHEMES:
# ax.plot(time_all[scheme][l][10:VIDEO_LEN], bw_robust_all[scheme][l][10:VIDEO_LEN])
# ax.plot(time_all[scheme][l][10:VIDEO_LEN], bw_true_all[scheme][l][10:VIDEO_LEN])
# colors = [COLOR_MAP(i) for i in np.linspace(0, 1, len(ax.lines))]
# for i,j in enumerate(ax.lines):
# # j.set_color(colors[i]) #
# plt.setp(j, color = colors[i], linestyle = LINE_STY[i], linewidth = 2.6) #, marker = HATCH[i]
# plt.title(l, fontsize = 16)
# # plt.ylabel('Throughputs Prediction (MBps)')
# SCHEMES_REW = ['BNN-based prediction (LB)', 'Harmonic mean (LB)', 'True']
# legend = ax.legend(SCHEMES_REW, loc=1, fontsize = 17)
# frame = legend.get_frame()
# frame.set_alpha(0)
# frame.set_facecolor('none')
# plt.ylabel('Throughputs Prediction (MBps)', fontsize = 20)
# # plt.xlabel("$QoE_{lin}$ improvement", fontsize = 18)
# plt.xticks(fontsize = 16)
# plt.yticks(fontsize = 16)
# ax.spines['top'].set_visible(False)
# ax.spines['right'].set_visible(False)
# ax.spines['bottom'].set_linewidth(2.5)
# ax.spines['left'].set_linewidth(2.5)
# plt.show()
# fig = plt.figure()
# ax = fig.add_subplot(311)
# for scheme in SCHEMES:
# ax.plot(time_all[scheme][l][:VIDEO_LEN], bit_rate_all[scheme][l][:VIDEO_LEN])
# colors = [COLOR_MAP(i) for i in np.linspace(0, 1, len(ax.lines))]
# for i,j in enumerate(ax.lines):
# j.set_color(colors[i])
# plt.title(l)
# plt.ylabel('bit rate selection (kbps)')
# ax = fig.add_subplot(312)
# for scheme in SCHEMES:
# ax.plot(time_all[scheme][l][:VIDEO_LEN], buff_all[scheme][l][:VIDEO_LEN])
# colors = [COLOR_MAP(i) for i in np.linspace(0, 1, len(ax.lines))]
# for i,j in enumerate(ax.lines):
# j.set_color(colors[i])
# plt.ylabel('buffer size (sec)')
# ax = fig.add_subplot(313)
# for scheme in SCHEMES:
# ax.plot(time_all[scheme][l][:VIDEO_LEN], bw_all[scheme][l][:VIDEO_LEN])
# colors = [COLOR_MAP(i) for i in np.linspace(0, 1, len(ax.lines))]
# for i,j in enumerate(ax.lines):
# j.set_color(colors[i])
# plt.ylabel('bandwidth (mbps)')
# plt.xlabel('time (sec)')
# SCHEMES_REW = []
# for scheme in SCHEMES:
# SCHEMES_REW.append(scheme + ': ' + str('%.3f' %np.sum(raw_reward_all[scheme][l][1:VIDEO_LEN])))
# ax.legend(SCHEMES_REW, loc=9, bbox_to_anchor=(0.5, -0.3), ncol=int(np.ceil(len(SCHEMES) / 3.0)))
# plt.show()
if __name__ == '__main__':
main()