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Plot_Hypmaxcut.py
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Plot_Hypmaxcut.py
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import matplotlib.pyplot as plt
import json
import os
import numpy as np
from scipy.optimize import curve_fit
# with open('./res/partition_new2.json') as f:
# partition=json.load(f)
#synthetic
path='../log/Hypermaxcut_syn_new.log'
with open(path) as f:
Log=f.readlines()
log_d={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0].split(':')[2]
time=float(temp1[1][1:-1])
res=float(temp2[1][3:-2])
res_th=float(temp3[1][3:-2])
log_d[name]={}
log_d[name]['time']=time
log_d[name]['res'] = abs(int(res))
log_d[name]['res_th'] = abs(int(res_th))
path="./data/hypergraph_data/synthetic/new/"+name
with open(path) as f:
file = f.read()
lines = file.split('\n')
info = lines[0].split(' ')
n = int(info[0])
m = int(info[1])
log_d[name]['n']=n
log_d[name]['m']=m
path='../log/Hypermaxcut_syn_new_Rand_l2.log'
with open(path) as f:
Log=f.readlines()
log_r={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0].split(':')[2]
time=float(temp1[1][1:-1])
res=float(temp2[1][3:-2])
res_th=float(temp3[1][3:-2])
log_r[name]={}
log_r[name]['time']=time
log_r[name]['res'] = abs(int(res))
log_r[name]['res_th'] = abs(int(res_th))
x_axis=np.array([log_d[name]['n'] for name in log_d])
y_axis=np.array([log_d[name]['res']/log_d[name]['m'] for name in log_d])
y_axis2=np.array([log_r[name]['res']/log_d[name]['m'] for name in log_r])
# y_axis_=np.array([log_d[name]['res_th']/log_d[name]['n'] for name in log_d])
# y_axis2_=np.array([log_g[name]['res_th']/log_d[name]['n'] for name in log_g])
y_axis_t=np.array([log_d[name]['time'] for name in log_d])
y_axis_t2=np.array([log_r[name]['time'] for name in log_r])
# y_axis_t3=np.array([log_g[name]['time'] for name in log_g])
nd=len(y_axis)
plotm=np.zeros([10,nd])
plotm[0,:]=x_axis
plotm[1,:]=y_axis
plotm[2,:]=y_axis2
plotm[3,:]=y_axis_t
plotm[4,:]=y_axis_t2
# plotm[5,:]=y_axis3
# plotm[6,:]=y_axis_t3
# plotm[7,:]=y_axis_
# plotm[8,:]=y_axis2_
plotms=plotm[:, plotm[0].argsort()]
plt.plot(plotms[0,:],plotms[2,:], marker='x', label='SA')
plt.plot(plotms[0,:],plotms[1,:], marker='o', label='HypOp')
plt.ylabel('Cut Size over the Number of Hyperedges')
plt.xlabel('Number of Nodes')
plt.legend(loc='lower right')
plt.savefig('../res/plots/Hypermaxcut_syn.png')
plt.show()
def model_ex(x,a, b):
return b*np.exp(a*x)
def model_l(x,a,b):
return b*(x)+a
popt, pcov = curve_fit(model_ex, plotms[0,:], plotms[4,:], p0=[0,0])
a_ex, b_ex= popt
x_model = np.linspace(min(plotms[0,:]), max(plotms[0,:]), 100)
y_model = model_ex(x_model, a_ex, b_ex)
popt, pcov = curve_fit(model_l, plotms[0,:], plotms[3,:], p0=[0,0])
# popt2, pcov2 = curve_fit(model_l, plotms[0,:], plotms[6,:], p0=[0,0])
a_l, b_l= popt
# a_l2, b_l2= popt2
y_model2 = model_l(x_model, a_l, b_l)
# y_model3 = model_l(x_model, a_l2, b_l2)
plt.scatter(plotms[0,:], plotms[4,:], label='SA')
plt.scatter(plotms[0,:], plotms[3,:], color='g', label='HypOp')
plt.plot(x_model, y_model, color='r', label='Exponential Curve Fit')
plt.plot(x_model, y_model2, color='y', label='Linear Curve Fit')
plt.ylabel('Run time (s)')
plt.xlabel('Number of Nodes')
plt.legend(loc='upper left')
plt.savefig('./res/plots/Hypermaxcut_syn_time.png')
plt.show()
# plt.plot(plotms[0,:],plotms[5,:], marker='x', label='ADAM')
# plt.plot(plotms[0,:],plotms[1,:], marker='o', label='HypOp')
# plt.ylabel('Cut Size over the Number of Nodes')
# plt.xlabel('Number of Nodes')
# plt.legend(loc='upper right')
# plt.show()
#APS
path='../log/Hypermaxcut_APS_3Y_f2.log'
with open(path) as f:
Log=f.readlines()
log_d={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0].split(':')[2]
time=float(temp1[1][1:-1])
res=float(temp2[1][3:-2])
res_th=float(temp3[1][3:-2])
log_d[name]={}
log_d[name]['time']=time
log_d[name]['res'] = abs(int(res))
log_d[name]['res_th'] = abs(int(res_th))
path="../data/hypergraph_data/APS/3years_Stanford/"+name
with open(path) as f:
file = f.read()
lines = file.split('\n')
info = lines[0].split(' ')
n = int(info[0])
m = int(info[1])
log_d[name]['n']=n
log_d[name]['m']=m
path='../log/Hypermaxcut_APS_3Y_Rand_l.log'
with open(path) as f:
Log=f.readlines()
log_r={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0].split(':')[2]
time=float(temp1[1][1:-1])
res=float(temp2[1][3:-2])
res_th=float(temp3[1][3:-2])
log_r[name]={}
log_r[name]['time']=time
log_r[name]['res'] = abs(int(res))
log_r[name]['res_th'] = abs(int(res_th))
path='../log/Hypermaxcut_APS_3Y_GD3.log'
with open(path) as f:
Log=f.readlines()
log_g={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0].split(':')[2]
time=float(temp1[1][1:-1])
res=float(temp2[1][3:-2])
res_th=float(temp3[1][3:-2])
log_g[name]={}
log_g[name]['time']=time
log_g[name]['res'] = abs(int(res))
log_g[name]['res_th'] = abs(int(res_th))
path='../log/Hypermaxcut_APS_3Y_GD.log'
with open(path) as f:
Log=f.readlines()
log_g2={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0].split(':')[2]
time=float(temp1[1][1:-1])
res=float(temp2[1][3:-2])
res_th=float(temp3[1][3:-2])
log_g2[name]={}
log_g2[name]['time']=time
log_g2[name]['res'] = abs(int(res))
log_g2[name]['res_th'] = abs(int(res_th))
x_axis=np.array([log_d[name]['n'] for name in log_d])
y_axis=np.array([log_d[name]['res']/log_d[name]['m'] for name in log_d])
y_axis2=np.array([log_r[name]['res']/log_d[name]['m'] for name in log_r])
y_axis3=np.array([log_g[name]['res']/log_d[name]['m'] for name in log_g])
y_axis_=np.array([log_d[name]['res_th']/log_d[name]['m'] for name in log_d])
y_axis2_=np.array([log_g[name]['res_th']/log_d[name]['m'] for name in log_g])
y_axis3_=np.array([log_g2[name]['res_th']/log_d[name]['m'] for name in log_g2])
y_axis_t=np.array([log_d[name]['time'] for name in log_d])
y_axis_t2=np.array([log_r[name]['time'] for name in log_r])
y_axis_t3=np.array([log_g[name]['time'] for name in log_g])
y_axis_t4=np.array([log_g2[name]['time'] for name in log_g2])
nd=len(y_axis)
plotm=np.zeros([11,nd])
plotm[0,:]=x_axis
plotm[1,:]=y_axis
plotm[2,:]=y_axis2
plotm[3,:]=y_axis_t
plotm[4,:]=y_axis_t2
plotm[5,:]=y_axis3
plotm[6,:]=y_axis_t3
plotm[7,:]=y_axis_
plotm[8,:]=y_axis2_
plotm[9,:]=y_axis3_
plotm[10,:]=y_axis_t4
plotms=plotm[:, plotm[0].argsort()]
plt.plot(plotms[0,:],plotms[2,:], marker='x', label='SA')
plt.plot(plotms[0,:],plotms[1,:], marker='o', label='HypOp')
plt.ylabel('Cut Size over the Number of Hyperedges')
plt.xlabel('Number of Nodes')
plt.legend(loc='lower right')
plt.savefig('./res/plots/Hypermaxcut_APS.png')
plt.show()
# plt.plot(plotms[0,:],plotms[4,:], marker='X', label='SA')
# plt.plot(plotms[0,:],plotms[3,:], marker='o', label='HypOp')
# plt.ylabel('Runtime (s)')
# plt.xlabel('Number of Nodes')
# plt.legend(loc='upper right')
# plt.show()
plt.plot(plotms[0,:],plotms[7,:], marker='x', label='HypOp')
plt.plot(plotms[0,:],plotms[8,:], marker='o', label='ADAM')
plt.ylabel('Cut Size over the Number of Hyperedges')
plt.xlabel('Number of Nodes')
plt.legend(loc='lower right')
plt.savefig('./res/plots/Hypermaxcut_APS_ADAM_l2.png')
plt.show()
plt.plot(plotms[0,:],plotms[7,:], marker='x', label='HypOp')
plt.plot(plotms[0,:],plotms[9,:], marker='o', label='ADAM')
plt.ylabel('Cut Size over the Number of Hyperedges')
plt.xlabel('Number of Nodes')
plt.legend(loc='lower right')
plt.savefig('./res/plots/Hypermaxcut_APS_ADAM.png')
plt.show()
def model_ex(x,a, b):
return b*np.exp(a*x)
def model_l(x,a,b):
return b*(x)+a
popt, pcov = curve_fit(model_ex, plotms[0,:], plotms[4,:], p0=[0,0])
a_ex, b_ex= popt
x_model = np.linspace(min(plotms[0,:]), max(plotms[0,:]), 100)
y_model = model_ex(x_model, a_ex, b_ex)
popt, pcov = curve_fit(model_l, plotms[0,:], plotms[3,:], p0=[0,0])
popt2, pcov2 = curve_fit(model_l, plotms[0,:], plotms[6,:], p0=[0,0])
popt3, pcov3 = curve_fit(model_l, plotms[0,:], plotms[10,:], p0=[0,0])
a_l, b_l= popt
a_l2, b_l2= popt2
a_l3, b_l3= popt3
y_model2 = model_l(x_model, a_l, b_l)
y_model3 = model_l(x_model, a_l2, b_l2)
y_model4 = model_l(x_model, a_l3, b_l3)
plt.scatter(plotms[0,:], plotms[4,:], label='SA')
plt.scatter(plotms[0,:], plotms[3,:], color='g', label='HypOp')
plt.plot(x_model, y_model, color='r', label='Exponential Curve Fit for SA')
plt.plot(x_model, y_model2, color='y', label='Linear Curve Fit for HypOp')
plt.ylabel('Run time (s)')
plt.xlabel('Number of Nodes')
plt.legend(loc='upper left')
plt.savefig('./res/plots/Hypermaxcut_APS_time_fit.png')
plt.show()
# plt.plot(plotms[0,:],plotms[5,:], marker='x', label='ADAM')
# plt.plot(plotms[0,:],plotms[1,:], marker='o', label='HypOp')
# plt.ylabel('Cut Size over the Number of Hyperedges')
# plt.xlabel('Number of Nodes')
# plt.legend(loc='upper right')
# plt.show()
plt.scatter(plotms[0,:], plotms[6,:], label='ADAM')
plt.scatter(plotms[0,:], plotms[3,:], color='g', label='HypOp')
plt.plot(x_model, y_model2, color='y', label='Linear Curve Fit for HypOp')
plt.plot(x_model, y_model3, color='r', label='Linear Curve Fit for ADAM')
plt.ylabel('Run time (s)')
plt.xlabel('Number of Nodes')
plt.legend(loc='upper left')
plt.savefig('./res/plots/Hypermaxcut_APS_ADAM_time_l.png')
plt.show()
plt.scatter(plotms[0,:], plotms[10,:], color='b', label='ADAM')
plt.scatter(plotms[0,:], plotms[3,:], color='g', label='HypOp')
plt.plot(x_model, y_model2, color='y', label='Linear Curve Fit for HypOp')
plt.plot(x_model, y_model4, color='r', label='Linear Curve Fit for ADAM')
plt.ylabel('Run time (s)')
plt.xlabel('Number of Nodes')
plt.legend(loc='upper left')
plt.savefig('./res/plots/Hypermaxcut_APS_ADAM_time.png')
plt.show()