-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathbaseline.py
155 lines (133 loc) · 5.51 KB
/
baseline.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.datasets import fetch_california_housing
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import SGD
import torch.utils.data as Data
import matplotlib.pyplot as plt
import seaborn as sns
import csv
import random
def data_progress():
cvxcnn_data = list()
cvxcnn_target = list()
with open("../data_generation/data.csv", "r") as csvfile:
reader = csv.reader(csvfile)
for line in reader:
cvxcnn_data.append(list(map(float, line[:-3])))
cvxcnn_target.append(list(map(float, line[-3:])))
# print("cvxcnn_data:",cvxcnn_data)
cvxcnn_data = np.array(cvxcnn_data)
cvxcnn_target = np.array(cvxcnn_target)
x_train,x_test,y_train,y_test = train_test_split(cvxcnn_data,cvxcnn_target,test_size=0.001,random_state=42)
print("x_train:",x_train.shape,"x_test:",x_test.shape,"y_train:",y_train.shape,"y_test:",y_test.shape) #(14448, 8) (6192, 8) (14448,) (6192,)
scale = StandardScaler()
x_train = scale.fit_transform(x_train)
# x_test = scale.transform(x_test)
train_xt = torch.from_numpy(x_train.astype(np.float32))
train_yt = torch.from_numpy(y_train.astype(np.float32))
test_xt = torch.from_numpy(x_test.astype(np.float32))
test_yt = torch.from_numpy(y_test.astype(np.float32))
print("train_xt:", train_xt)
print("train_yt:", train_yt)
train_data = Data.TensorDataset(train_xt,train_yt)
test_data = Data.TensorDataset(test_xt,test_yt)
train_loader = Data.DataLoader(dataset=train_data,batch_size=1,shuffle=True,num_workers=0)
# print("train_loader:", train_loader)
idx = random.randint(0,299)
#idx = 249，132，272，160
idx = 1
print("idx:",idx)
single_data = np.array([cvxcnn_data[idx].tolist()])
single_target = cvxcnn_target[idx]
single_data = scale.transform(single_data)
single_data = torch.from_numpy(single_data.astype(np.float32))
return train_loader, train_xt, train_yt, test_xt, test_yt,y_test,single_data,single_target
#test_loader = Data.DataLoader(dataset=test_data,batch_size=64,shuffle=True,num_workers=0)
class MLPPregression(nn.Module):
def __init__(self):
super(MLPPregression, self).__init__()
self.hidden1 = nn.Linear(in_features=18,out_features=200,bias=True)
self.hidden2 = nn.Linear(200,200)
self.hidden3 = nn.Linear(200,100)
self.predict = nn.Linear(100,3)
def forward(self,x):
x = F.relu(self.hidden1(x))
x = F.relu(self.hidden2(x))
x = F.relu(self.hidden3(x))
output = self.predict(x)
# output = torch.sigmoid(self.predict(x))
return output
def custom_mse(predicted, target):
total_mse = 0
# print("predicted:", predicted)
# print("target:", target)
for i in range(target.shape[1]):
# print("predicted[i]:", predicted.T[i])
total_mse+=nn.MSELoss()(predicted.T[i], target.T[i])
return total_mse
def training_progress(train_loader, train_xt, train_yt, test_xt, test_yt,y_test,single_data,single_target):
epoch_num = 100
cvxcnnreg = MLPPregression()
optimizer = SGD(cvxcnnreg.parameters(),lr=0.0006,weight_decay=0.0001)
loss_func = nn.MSELoss() #均方误差损失函数
train_loss_all = []
single_tp = []
# sing_feat, w, G, v, p_max = single_instance_feat()
# print("s_feat_st:", s_feat_st)
# optimal_value = np.sum(single_target)
optimal_value = single_target
optimal_value = [optimal_value]*epoch_num
# print("optimal_value:", optimal_value)
for epoch in range(epoch_num):
train_loss = 0
train_num = 0
for step,(b_x,b_y) in enumerate(train_loader):
# print("step:", step)
output = cvxcnnreg(b_x)
# print("output:", output)
# loss = loss_func(output,b_y)
loss = custom_mse(output, b_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item() * b_x.size(0)
train_num += b_x.size(0)
train_loss_all.append(train_loss / train_num)
single_predict = cvxcnnreg(single_data)
p = single_predict.data.numpy()
# print("====p:",p)
# single_tp.append(np.sum(p))
single_tp.append(p[0])
print("train_loss_all:", train_loss / train_num)
# plot loss
plt.figure(figsize=(10,4))
plt.subplot(1, 2, 1)
# plt.figure(figsize=(13,9))
plt.plot(train_loss_all,c='cornflowerblue',marker='o',markerfacecolor='none',label="Training loss")
plt.legend(fontsize=10)
plt.xlabel("epoch",fontsize=10)
plt.ylabel("loss",fontsize=10)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
# plt.savefig("./loss.pdf")
# plt.show()
# plt.figure(figsize=(13, 9))
plt.subplot(1, 2, 2)
plt.plot(single_tp, label="Supervised learning")
plt.plot(optimal_value, linewidth=2, linestyle="-." , label="Optimal value")
# plt.legend(fontsize=10)
plt.xlabel("epoch", fontsize=10)
plt.ylabel("$w^Tr$", fontsize=10)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
plt.savefig("./sinr_sum.pdf")
plt.show()
if __name__ == '__main__':
train_loader, train_xt, train_yt, test_xt, test_yt,y_test,single_data,single_target = data_progress()
training_progress(train_loader, train_xt, train_yt, test_xt, test_yt, y_test,single_data,single_target)