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thesis_subplotter.py
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thesis_subplotter.py
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import os
import pickle
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rcParams
markers = [
'o', 'p', 'v', 's', '*'
]
SCALE = 4.5
# SCALE = 2.5
FONT_SIZE = 20
LINE_STYLE = '-'
MARKER_SIZE = 10
TICK_SIZE = 16
def subplotall(args):
def define_style(rcParams):
rcParams['figure.figsize'] = SCALE*4, SCALE*1.7
rcParams['figure.subplot.wspace'] = 0.2
rcParams['figure.subplot.hspace'] = 0.15
rcParams['lines.markersize'] = MARKER_SIZE
rcParams['xtick.labelsize'] = TICK_SIZE
rcParams['ytick.labelsize'] = TICK_SIZE
rcParams['axes.labelsize'] = FONT_SIZE
rcParams['axes.titlesize'] = FONT_SIZE
rcParams['legend.fontsize'] = FONT_SIZE-4
rcParams['font.sans-serif'] = 'Microsoft YaHei'
rcParams['axes.grid'] = True
define_style(rcParams)
# plt.style.use('seaborn')
# plt.rc('axes', labelsize=20, titlesize=20)
# plt.rc('xtick', labelsize=15)
# plt.rc('ytick', labelsize=15)
# plt.rc('legend', fontsize=19)
linestyles = ('d--', 'o--', 'd-', 'o-', '*--')
fig, ax_matrix = plt.subplots(2, 4)
# fig.subplots_adjust(left=0.05, right=0.98,top=0.95, bottom=0.16, hspace=0.3,wspace=0.31)
for i in range(len(args.lams)):
sbe_name = 'sbe'+'_'+args.network+'_a'+str(args.attack)+'_lam'+str(args.lams[i])+'.pkl'
ace_name = 'ce'+'_'+args.network+'_a'+str(args.attack)+'_lam'+str(args.lams[i])+'.pkl'
file_dir = os.path.dirname(os.path.abspath(__file__))
dir_png_path = os.path.join(file_dir, 'record')
sbe_path = os.path.join(dir_png_path, sbe_name)
ace_path = os.path.join(dir_png_path, ace_name)
f = open(sbe_path, 'rb')
asbe = np.array(pickle.load(f))
f.close()
f = open(ace_path, 'rb')
ace = np.array(pickle.load(f))
f.close()
# fig.add_subplot(2, len(args.lams), i+1, xlabel='Step', ylabel='MSBE', title=r'$\lambda=$%.1g' %args.lams[i])
axes = ax_matrix[0][i]
for k in range(np.shape(asbe)[-1]):
y = asbe[:,:,k]
y_mean = np.mean(y, 0)
x = np.arange(len(y_mean))
axes.set_title(r'$\lambda=$%.1g' %args.lams[i])
axes.plot(x, y_mean, linestyles[k], linewidth=2.25, markevery=30)
plt.ticklabel_format(axis='y', style='sci', scilimits=(-2,2))
# if args.lnk:
# fig.add_subplot(2, len(args.lams), i+1+len(args.lams), xlabel='Step', ylabel=r'MCE$\times k/\ln(k)$')
# else:
# fig.add_subplot(2, len(args.lams), i+1+len(args.lams), xlabel='Step', ylabel='MCE')
axes = ax_matrix[1][i]
for k in range(np.shape(ace)[-1]):
y = ace[:,:,k]
y_mean = np.mean(y, 0)
try:
if args.lnk:
for j in np.arange(1,len(y_mean)):
y_mean[j] = y_mean[j]*(j+1)/np.log(j+1)
except:
pass
x = np.arange(len(y_mean))
axes.plot(x, y_mean, linestyles[k], linewidth=2.5, markevery=30)
axes.set_yscale('log')
axes.set_xlabel('迭代次数')
# plt.ticklabel_format(axis='y', style='sci', scilimits=(-2,2))
ax_matrix[0][0].set_ylabel('MSBE')
ax_matrix[1][0].set_ylabel('一致误差')
fig.legend(
(r'加权平均（无攻击）',
r'截尾均值（无攻击）',
r'加权平均（有攻击）',
r'截尾均值（有攻击）',
r'无通信'),
loc='lower left', bbox_to_anchor=(0.12, -0.05), fancybox=False, shadow=False,
ncol=5, borderaxespad=0., frameon=True)
# fig.tight_layout()
pic_name = 'all_'+args.network+'_a'+str(args.attack)
file_dir = os.path.dirname(os.path.abspath(__file__))
dir_png_path = os.path.join(file_dir, 'thesis-figure', 'png')
dir_pdf_path = os.path.join(file_dir, 'thesis-figure', 'pdf')
if not os.path.isdir(dir_pdf_path):
os.makedirs(dir_pdf_path)
if not os.path.isdir(dir_png_path):
os.makedirs(dir_png_path)
pic_png_path = os.path.join(dir_png_path, pic_name + '.png')
pic_pdf_path = os.path.join(dir_pdf_path, pic_name + '.pdf')
plt.savefig(pic_png_path, format='png', bbox_inches='tight')
plt.savefig(pic_pdf_path, format='pdf', bbox_inches='tight')
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='Plotter for robust TD')
parser.add_argument('--network', type=str, default='complete',
help='name of the network (h1b1, h3b1, b3b2, renyi, complete)')
parser.add_argument('--attack', type=int, default=2,
help='Type of attack')
parser.add_argument('--lams', type=float, nargs='+',
help='lambda list', default=[0.0,0.3,0.6,0.9])
parser.add_argument('--lnk', action='store_true',
help='whether to divide aggregate ce by ln(epoch) or epoch')
args = parser.parse_args()
subplotall(args)