add negative fix weight

examples-probability-sample/draw_boundary.py

+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 = [1,0.1,0.05,0.01,0]
+data_values = ['WIKI','LASTFM','WikiTalk','DGraphFin']  # 存储从文件中读取的数据
+seed = ['13357','12347','63377','53473',' 54763']
+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
+topk=0.01
+mem='all_update'#'historical'
+model='TGN'
+for sd in seed :
+    for data in data_values:
+        ap_list = []
+        comm_list = []
+        for p in probability_values:
+            if data == 'WIKI' or data =='LASTFM':
+                model = 'TGN'
+            else:
+                model = 'TGN_large'
+            if p == 1:
+                file = 'all_{}/{}/{}/{}-{}-{}-{}-recent.out'.format(sd,data,model,partitions,partition,topk,mem)
+            else:
+                file = 'all_{}/{}/{}/{}-{}-{}-{}-boundery_recent_decay-{}.out'.format(sd,data,model,partitions,partition,topk,mem,p)
+            prefix = "val ap:"
+            max_val_ap = 0
+            test_ap = 0
+            with open(file, 'r') as file:
+                for line in file:
+                    if line.find(prefix)!=-1:
+                        pos = line.find(prefix)+len(prefix)
+                        posr = line.find(' ',pos)
+                        #print(line[pos:posr])
+                        val_ap = float(line[pos:posr])
+                    
+                        pos = line.find("test ap ")+len("test ap ")
+                        posr = line.find(' ',pos)
+                        #print(line[pos:posr])
+                        _test_ap = float(line[pos:posr])
+                        if(val_ap>max_val_ap):
+                            max_val_ap = val_ap
+                            test_ap = _test_ap
+                ap_list.append(test_ap)
+        print('data {} seed {}  ap: {}'.format(data,sd,ap_list))
+        # 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-probability-sample/test_all.sh

@@ -14,7 +14,7 @@ probability_params=("0.1" "0" "0.05" "0.01")
 sample_type_params=("boundery_recent_decay" "recent")
 #sample_type_params=("recent" "boundery_recent_decay") #"boundery_recent_uniform")
 #memory_type=("all_update" "p2p" "all_reduce" "historical" "local")
-memory_type=( "all_update" "historical" "local")
+memory_type=( "all_update")
 #memory_type=("local" "all_update" "historical" "all_reduce")
 shared_memory_ssim=("0.3" "0.7")
 #data_param=("WIKI" "REDDIT" "LASTFM" "WikiTalk")