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BTS-MTGNN
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fix weight for training when boundary sample

parent 54c0c86c
Showing with 4 additions and 184 deletions
examples/all/draw_boundary.py deleted 100644 → 0
import matplotlib.pyplot as plt
import numpy as np
import torch
# 读取文件内容
ssim_values = [0, 0.1, 0.2, 0.3, 0.4, 2] # 假设这是你的 ssim 参数值
probability_values = [1,0.5,0.1,0.05,0.01,0]
data_values = ['WIKI_3','LASTFM_3','WikiTalk','StackOverflow'] # 存储从文件中读取的数据
partition = 'ours'
# 从文件中读取数据，假设数据存储在文件 data.txt 中
#all/"$data"/"$partitions"-ours_shared-0.01-"$mem"-"$ssim"-"$sample".out
partitions=4
topk=0
mem='all_update'#'historical'
model='TGN'
for data in data_values:
ap_list = []
comm_list = []
for p in probability_values:
file = '{}/{}/{}-{}-{}-{}-boundery_recent_uniform-{}.out'.format(data,model,partitions,partition,topk,mem,p)
prefix = 'best test AP:'
cnt = 0
sum = 0
with open(file, 'r') as file:
for line in file:
if line.startswith(prefix):
ap = float(line.lstrip(prefix).split(' ')[0])
pos = line.find('remote node number tensor')
if(pos!=-1):
posr = line.find(']',pos+2+len('remote node number tensor'),)
#print(line,line[pos+2+len('remote node number tensor'):posr])
comm = int(line[pos+2+len('remote node number tensor'):posr])
#print()
sum = sum+comm
cnt = cnt+1
#print(comm)
ap_list.append(ap)
comm_list.append(sum/cnt*4)
# 绘制柱状图
print('{} TestAP={}\n'.format(data,ap_list))
bar_width = 0.4
#shared comm tensor
# 设置柱状图的位置
bars = range(len(probability_values))
# 绘制柱状图
plt.bar([b for b in bars], ap_list, width=bar_width)
# 绘制柱状图
plt.ylim([0.9,1])
plt.xticks([b for b in bars], probability_values)
plt.xlabel('probability')
plt.ylabel('Test AP')
plt.title('{}({} partitions)'.format(data,partitions))
plt.savefig('boundary_AP_{}_{}_{}.png'.format(data,partitions,model))
plt.clf()
print(comm_list)
plt.bar([b for b in bars], comm_list, width=bar_width)
# 绘制柱状图
plt.xticks([b for b in bars], probability_values)
plt.xlabel('probability')
plt.ylabel('Communication volume')
plt.title('{}({} partitions)'.format(data,partitions))
plt.savefig('boundary_comm_{}_{}_{}.png'.format(data,partitions,model))
plt.clf()
if partition == 'ours_shared':
partition0 = 'ours'
else:
partition0=partition
for p in probability_values:
file = '{}/{}/test_{}_{}_{}_0_boundery_recent_uniform_{}_all_update_2.pt'.format(data,model,partition0,topk,partitions,float(p))
val_ap = torch.tensor(torch.load(file))[:,0]
epoch = torch.arange(val_ap.shape[0])
#绘制曲线图
plt.plot(epoch,val_ap, label='probability={}'.format(p))
plt.xlabel('Epoch')
plt.ylabel('Val AP')
plt.title('{}({} partitions)'.format(data,partitions))
# plt.grid(True)
plt.legend()
plt.savefig('{}_{}_{}_boundary_Convergence_rate.png'.format(data,partitions,model))
plt.clf()
examples/all/draw_ssim.py deleted 100644 → 0
import matplotlib.pyplot as plt
import numpy as np
import torch
# 读取文件内容
ssim_values = [-1,0.3,0.5,0.7,2] # 假设这是你的 ssim 参数值
data_values = ['WIKI','LASTFM','WikiTalk','REDDIT','LASTFM','DGraphFin'] # 存储从文件中读取的数据
partition = 'ours_shared'
# 从文件中读取数据，假设数据存储在文件 data.txt 中
#all/"$data"/"$partitions"-ours_shared-0.01-"$mem"-"$ssim"-"$sample".out
partitions=4
model = 'TGN'
topk=0.01
mem='historical'
for data in data_values:
ap_list = []
comm_list = []
for ssim in ssim_values:
if ssim == 2:
file = '{}/{}/{}-{}-{}-local-recent.out'.format(data,model,partitions,partition,topk)
elif ssim == -1:
file = '{}/{}/{}-{}-{}-all_update-recent.out'.format(data,model,partitions,partition,topk)
else:
file = '{}/{}/{}-{}-{}-{}-{}-recent.out'.format(data,model,partitions,partition,topk,mem,ssim)
prefix = 'best test AP:'
with open(file, 'r') as file:
for line in file:
if line.startswith(prefix):
ap = float(line.lstrip(prefix).split(' ')[0])
pos = line.find('shared comm tensor')
if(pos!=-1):
comm = int(line[pos+2+len('shared comm tensor'):len(line)-3])
print(ap)
ap_list.append(ap)
comm_list.append(comm)
print('{} TestAP={}\n'.format(data,ap_list))
# 绘制柱状图
bar_width = 0.4
#shared comm tensor
print('{} TestAP={}\n'.format(data,ap_list))
# 设置柱状图的位置
bars = range(len(ssim_values))
# 绘制柱状图
plt.bar([b for b in bars], ap_list, width=bar_width)
# 绘制柱状图
plt.xticks([b for b in bars], ssim_values)
plt.xlabel('SSIM threshold Values')
plt.ylabel('Test AP')
#if(data=='WIKI'):
# plt.ylim([0.97,1])
plt.title('{}({} partitions)'.format(data,partitions))
plt.savefig('ssim_{}_{}_{}.png'.format(data,partitions,model))
plt.clf()
plt.bar([b for b in bars], comm_list, width=bar_width)
# 绘制柱状图
plt.xticks([b for b in bars], ssim_values)
plt.xlabel('SSIM threshold Values')
plt.ylabel('Communication volume')
plt.title('{}({} partitions)'.format(data,partitions))
plt.savefig('ssim_comm_{}_{}_{}.png'.format(data,partitions,model))
plt.clf()
if partition == 'ours_shared':
partition0 = 'ours'
else:
partition0=partition
for ssim in ssim_values:
if ssim == 2:
file = '{}/{}/test_{}_{}_{}_0_recent_0.1_local_2.pt'.format(data,model,partition0,topk,partitions,)
elif ssim == -1:
file = '{}/{}/test_{}_{}_{}_0_recent_0.1_all_update_2.pt'.format(data,model,partition0,topk,partitions,)
else:
file = '{}/{}/test_{}_{}_{}_0_recent_0.1_{}_{}.pt'.format(data,model,partition0,topk,partitions,mem,float(ssim))
val_ap = torch.tensor(torch.load(file))[:,0]
print(val_ap)
epoch = torch.arange(val_ap.shape[0])
#绘制曲线图
#print(val_ap)
if ssim == -1:
plt.plot(epoch,val_ap, label='all-update')
elif ssim == 2:
plt.plot(epoch,val_ap, label='local')
else:
plt.plot(epoch,val_ap, label='ssim = {}'.format(ssim))
if(data=='WIKI'):
plt.ylim([0.85,0.90])
plt.xlabel('Epoch')
plt.ylabel('Val AP')
plt.title('{}({} partitions)'.format(data,partitions))
# plt.grid(True)
plt.legend()
plt.savefig('{}_{}_{}_ssim_Convergence_rate.png'.format(data,partitions,model))
plt.clf()
examples/test_all.sh
#!/bin/bash #!/bin/bash
# 定义数组变量 # 定义数组变量
addr="192.168.1.105" addr="192.168.1.106"
partition_params=("ours" ) partition_params=("ours" )
#"metis" "ldg" "random") #"metis" "ldg" "random")
#("ours" "metis" "ldg" "random") #("ours" "metis" "ldg" "random")
...@@ -10,14 +10,14 @@ node_per="4" ...@@ -10,14 +10,14 @@ node_per="4"
nnodes="1" nnodes="1"
node_rank="0" node_rank="0"
probability_params=("0.1" "0" "0.05" "0.01") probability_params=("0.1" "0" "0.05" "0.01")
sample_type_params=("boundery_recent_decay" "recent") sample_type_params=("boundery_recent_decay")
#sample_type_params=("recent" "boundery_recent_decay") #"boundery_recent_uniform") #sample_type_params=("recent" "boundery_recent_decay") #"boundery_recent_uniform")
#memory_type=("all_update" "p2p" "all_reduce" "historical" "local") #memory_type=("all_update" "p2p" "all_reduce" "historical" "local")
memory_type=( "historical" "local" "all_update") memory_type=( "all_update")
#memory_type=("local" "all_update" "historical" "all_reduce") #memory_type=("local" "all_update" "historical" "all_reduce")
shared_memory_ssim=("0.3" "0.7") shared_memory_ssim=("0.3" "0.7")
#data_param=("WIKI" "REDDIT" "LASTFM" "WikiTalk") #data_param=("WIKI" "REDDIT" "LASTFM" "WikiTalk")
data_param=("TaoBao" "StackOverflow" "GDELT") data_param=("WIKI" "LASTFM" "WikiTalk")
#data_param=("WIKI" "REDDIT" "LASTFM" "DGraphFin" "WikiTalk" "StackOverflow") #data_param=("WIKI" "REDDIT" "LASTFM" "DGraphFin" "WikiTalk" "StackOverflow")
#data_param=("WIKI" "REDDIT" "LASTFM" "WikiTalk" "StackOverflow") #data_param=("WIKI" "REDDIT" "LASTFM" "WikiTalk" "StackOverflow")
#data_param=("REDDIT" "WikiTalk") #data_param=("REDDIT" "WikiTalk")
......
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