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learning_model_v3.py
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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
import scipy
import csv
torch.random.seed()
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,)
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))
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
class Cvxnnregression(nn.Module):
def __init__(self):
super(Cvxnnregression, self).__init__()
self.neuron_num = 100
self.hidden1 = nn.Linear(in_features=9,out_features=self.neuron_num,bias=True) #200
# self.hidden2 = nn.Linear(self.neuron_num,self.neuron_num) #200*200
# self.hidden3 = nn.Linear(self.neuron_num,self.neuron_num) #200*100
# self.r_layer = nn.Linear(self.neuron_num,4)
def forward(self,x):
x0 = x
# p_bar = x0[0][15:18]
p_bar = torch.reshape(x0[0][15:18], (3,1))
p = p_bar
#layer 1
# x = self.single_condensationL(x0,p)
alpha = F.relu(nn.Linear(9, self.neuron_num)(x0[0][0:9]))
alpha = F.relu(nn.Linear(self.neuron_num, 9)(alpha))
p = self.cvxL_unroll(x0, alpha, p)
# layer 2
# x = self.single_condensationL(x0, p)
# alpha = F.relu(nn.Linear(3, self.neuron_num)(x))
# alpha = F.relu(nn.Linear(self.neuron_num, 9)(alpha))
# p = self.cvxL_unroll(x0, alpha, p)
# # layer 3
# x = self.single_condensationL(x0, p)
# alpha = F.relu(nn.Linear(3, self.neuron_num)(x))
# alpha = F.relu(nn.Linear(self.neuron_num, 9)(alpha))
# p = self.cvxL_unroll(x0, alpha, p)
return p.t()
def single_condensationL(self,x0,p):
G = torch.reshape(x0[0][0:9], (3,3)) / 0.05
return torch.mm(G,p).t()
def cvxL_unroll(self,x0,alpha,p_bar): # inital p: p_bar
p = p_bar
# layer 1
p = self.unrollL(x0,alpha,p)
# print("unrollLp:",p)
# p = p_bar-F.relu(p_bar-p)
# p = F.relu(p)
# layer 2
# p = self.unrollL(x0, alpha, p)
# # print("unrollLp:",p)
# p = p_bar - F.relu(p_bar - p)
# p = F.relu(p)
# # layer 2
# p = self.unrollL(x0, alpha, p)
# # print("unrollLp:",p)
# p = p_bar - F.relu(p_bar - p)
# p = F.relu(p)
#
# # layer 2
# p = self.unrollL(x0, alpha, p)
# # print("unrollLp:",p)
# p = p_bar - F.relu(p_bar - p)
# p = F.relu(p)
return p
def unrollL(self,x0,alpha,p):
G = torch.reshape(x0[0][0:9], (3, 3)) / 0.05
G = G - torch.diag(torch.diag(G))
w = torch.reshape(x0[0][9:12], (3, 1))
alpha = torch.reshape(alpha, (3,3))
# print("p:",p)
h = torch.mm(G, p) + 1
hw = torch.cat((h, torch.mm(w.t(), G).t(),torch.mm(w.t(),alpha).t()), 0)
m = nn.Linear(9, self.neuron_num)(hw.t())
# print("m0:",m)
m = nn.Linear(self.neuron_num, self.neuron_num)(m)
m = nn.Linear(self.neuron_num, 3)(m).t()
# print("m1:",m)
# p = torch.mm(w.t(),alpha).t() / m
# print("unrollLp:",p)
return p
def custom_mse(predicted, target):
total_mse = 0
# target = torch.tensor([[0.,0.,0.,0.]]) # add this line for unsupervised learning
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 = 30
cvxcnnreg = Cvxnnregression()
optimizer = SGD(cvxcnnreg.parameters(),lr=0.0015,weight_decay=0.0001)
loss_func = nn.MSELoss() #均方误差损失函数
train_loss_all = []
single_tp = []
optimal_value = single_target.tolist()
optimal_value = [optimal_value]*epoch_num
# print("optimal_value:", optimal_value)
for epoch in range(epoch_num):
print("epoch:", epoch)
train_loss = 0
train_num = 0
for step,(b_x,b_y) in enumerate(train_loader):
# print("step:", step)
p_predict = cvxcnnreg(b_x)
# print("output:", output)
# loss = loss_func(output,b_y)
loss = custom_mse(p_predict, 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].tolist())
print("train_loss_all:", train_loss / train_num)
print("train_loss_all:",train_loss_all)
return cvxcnnreg, single_tp,optimal_value
def plot_power(single_tp,optimal_value):
single_tp = np.array(single_tp)
# print("single_tp:", single_tp)
optimal_value = np.array(optimal_value)
# plot loss
plt.figure(figsize=(8,6))
color_choice = ['red','blue','green','purple']
for i in range(3):
print("i:",i)
plt.plot(single_tp[:,i], label="Algorithm 3", color = color_choice[i], alpha=0.7)
plt.plot(optimal_value[:,i], linewidth=2, linestyle="-." , label="Ground truth",color = color_choice[i],alpha=0.7)
# plt.legend(fontsize=14)
plt.xlabel("epoch", fontsize=14)
plt.ylabel("power(W)", fontsize=14)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.savefig("cnncvxl.pdf")
plt.show()
if __name__ == '__main__':
run_time = 1
obj_list = []
train_loader, train_xt, train_yt, test_xt, test_yt, y_test, single_data, single_target = data_progress()
if run_time == 1:
cvxcnnreg, single_tp,optimal_value = training_progress(train_loader, train_xt, train_yt, test_xt, test_yt, y_test,single_data,single_target)
plot_power(single_tp, optimal_value)
if run_time > 1:
for i in range(run_time):
cvxcnnreg, single_tp,optimal_value = training_progress(train_loader, train_xt, train_yt, test_xt, test_yt,
y_test, single_data, single_target)
single_predict, r_predict = cvxcnnreg(single_data)
p = single_predict.data.numpy()
obj_list.append(np.sum(p[0]))
print(obj_list)
add_row = []
with open('../cvxL_res_2by2/obj_list_cnn.csv', 'w', newline='') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=' ',
quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow(obj_list)