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plot_delay_oriented_twin.py
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plot_delay_oriented_twin.py
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import os
import sys
import scipy.io as sio
import scipy.sparse as sp
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
import seaborn as sns
import re
numeric_const_pattern = r"""
[-+]?
(?:
(?: \d* \. \d+ )
|
(?: \d+ \.? )
)
(?: [Ee] [+-]? \d+ ) ?
"""
rx = re.compile(numeric_const_pattern, re.VERBOSE)
header = ['Config', 'n', 'f', 't', 'load','q_med', 'q_95', 'q_avg', 'd_avg', 'u_gcn','run','loss a', 'loss c', 'ratio',
'e']
header_soj = ['Config', 'n', 'f', 't', 'load','q_med', 'q_95', 'q_avg', 'd_avg', 'u_gcn', 'sj_avg', 'sj_med', 'sj_95p',
'run', 'ratio', 'e']
padr = pd.read_csv('./wireless/graph_centrality.csv', index_col='graph')
padr = padr["PADR"]
dict_out_l1 = {
"Star30": "./wireless/star30_0.07_l1_GCNBP2_qr_test.out",
"Star20": "./wireless/star20_0.07_l1_GCNBP2_qr_test.out",
"Star10": "./wireless/star10_0.07_l1_GCNBP2_qr_test.out",
"BA-m1": "./wireless/ba1_0.07_l1_GCNBP2_qr_test.out",
"BA-m2": "./wireless/ba2_0.07_l1_GCNBP2_qr_test.out",
"Tree": "./wireless/tree_0.07_l1_GCNBP2_qr_test.out",
# "Tree": "./wireless/tree-line_0.07_l1_GCNBP2_qr_test.out",
"ER": "./wireless/er_0.07_l1_GCNBP2_qr_test.out",
# "poisson": "./wireless/poisson_0.07_l1_GCNBP2_test.out",
"BA-mix": "./wireless/bamix_0.07_l1_GCNBP2_qr_test.out"
}
dict_twin_l1 = {
"Star30": "./wireless/star30_0.07_l1_GDPGsr_qr_test.out",
"Star20": "./wireless/star20_0.07_l1_GDPGsr_qr_test.out",
"Star10": "./wireless/star10_0.07_l1_GDPGsr_qr_test.out",
"BA-m1": "./wireless/ba1_0.07_l1_GDPGsr_qr_test.out",
"BA-m2": "./wireless/ba2_0.07_l1_GDPGsr_qr_test.out",
"Tree": "./wireless/tree_0.07_l1_GDPGsr_qr_test.out",
# "Tree": "./wireless/tree-line_0.07_l1_GDPGsr_qr_test.out",
"ER": "./wireless/er_0.07_l1_GDPGsr_qr_test.out",
# "poisson": "./wireless/poisson_0.07_l1_GDPGsr_test.out",
"BA-mix": "./wireless/bamix_0.07_l1_GDPGsr_qr_test.out"
}
dict_df = {}
results = {}
for item in ['q_med', 'q_95', 'q_avg','u_gcn']:
df_tmp = pd.DataFrame({item:[],'graph':[],'PADR':[],'baseline':[],'train':[]})
for key in dict_out_l1:
fullpath = dict_out_l1[key]
file = fullpath.split('/')[-1]
if file.endswith(".out"):
print(fullpath)
else:
continue
df = pd.read_csv(fullpath, header=None)
df.columns = header
for col in header[4:]:
df[col] = pd.to_numeric(df[col].str.replace('s','').str.split(' ').str[-1], errors='coerce')
results[fullpath] = df
df_item = pd.DataFrame([])
df_item[item] = df[item]
df_item['graph'] = "{}\n{}".format(key, padr[key])
df_item['PADR'] = padr[key]
df_item['baseline'] = 'queue'
df_item['train'] = 'Lookahead RL [Zhao22]'
df_tmp = df_tmp.append(df_item)
for key in dict_twin_l1:
fullpath = dict_twin_l1[key]
file = fullpath.split('/')[-1]
# fullpath = os.path.join(result_dir, file)
if file.endswith(".out"):
print(fullpath)
else:
continue
df = pd.read_csv(fullpath, header=None)
df.columns = header_soj
for col in header_soj[4:]:
df[col] = pd.to_numeric(df[col].str.replace('s','').str.split(' ').str[-1], errors='coerce')
results[fullpath] = df
df_item = pd.DataFrame([])
df_item[item] = df[item]
df_item['graph'] = "{}\n{}".format(key, padr[key])
df_item['PADR'] = padr[key]
df_item['baseline'] = 'queue'
df_item['train'] = 'GDPG-Twin'
df_tmp = df_tmp.append(df_item)
dict_df[item] = df_tmp
fig, axs = plt.subplots(2, 1, sharex='all', figsize=(6, 4))
toplot = ['q_med', 'q_avg', 'u_gcn']
# toplot = ['q_95', 'q_med', 'q_avg', 'sj_95p', 'sj_med', 'sj_avg']
xlabels = []
padr = padr.sort_values()
for index, val in padr.items():
if index in dict_out_l1.keys():
xlabels.append("{}\n{}".format(index, val))
plotitems = ['Median', 'Mean', '$95^{th}$']
ylims = [[], [], []]
for i in range(2):
item = toplot[i]
df_tmp = dict_df[item]
ax = axs[i%3]
if i <3:
x_offset = -0.2
else:
x_offset = 0.2
# boxplot = df_tmp.boxplot(column=[item], by='PADR', rot=0, ax=ax,
# color=dict(boxes='b', whiskers='k', medians='r', caps='k'),
# showmeans=True, return_type='dict',
# widths=0.3, positions=np.arange(1, 9) + x_offset)
flierprops = dict(marker='o', markerfacecolor='None', markersize=3,
linestyle='none')
ax = sns.boxplot(x="PADR", y=item, hue="train",
data=df_tmp, flierprops=flierprops,
color=dict(boxes='b', whiskers='k', medians='r', caps='k'),
ax=ax, palette="tab20", showmeans=True)
ax.set_title('')
ax.set_ylabel(r'{} Backlog'.format(plotitems[i%3]), fontsize=12)
# boxplot.set_xlabel('Topology')
ax.set_xlabel('')
ax.set_xticks(list(range(0, 8)))
ax.set_xticklabels(xlabels, fontsize=12)
ax.set_ylim(0.48, 1.22)
ax.grid(True)
if i == 0:
ax.get_legend().remove()
# ax.legend(['RL-lookahead', "GDPG-Twin"])
# df_means = df_tmp.groupby('PADR').mean()
# x = 0
# for index, row in df_means.iterrows():
# y = row[item]
# text = '{:.3f}'.format(y)
# ax.annotate(text, xy=(x + x_offset, y),
# xytext=(0, -15), textcoords="offset pixels",
# color='g',
# fontsize=12,
# horizontalalignment="center",
# verticalalignment="top")
# x += 1
print(item)
fig.suptitle('')
fig.subplots_adjust(wspace=0)
plt.tight_layout()
fig.subplots_adjust(hspace=0)
plt.subplots_adjust(left=0.09, right=0.995, top=0.995, bottom=0.11)
plt.savefig("./output/delay_oriented_qr_by_topology_twin.png",
dpi = 300, # facecolor='w', edgecolor='w',
orientation = 'portrait',
format = 'png')
plt.savefig("./output/delay_oriented_qr_by_topology_twin.pdf",
dpi = 300, # facecolor='w', edgecolor='w',
orientation = 'portrait',
format = 'pdf')
print("done")
plt.close(fig)