#!/usr/bin/env python
import argparse
from ast import arg
import torch
import math
import numpy as np
import pandas as pd
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import copy
import os
import sys
import torch.utils.data as Data
#from optuna import TrialState
import optuna
#from get_data import get_mape
#from model import BiLSTM_AttentionModel
from itertools import chain
#from sklearn.preprocessing import MinMaxScaler
import torch
from scipy.interpolate import make_interp_spline
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
import optuna
from matplotlib import pyplot
from tqdm import tqdm
from torch.optim.lr_scheduler import StepLR
from tqdm import tqdm
#from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error,mean_absolute_percentage_error
import torch
from torch import nn
'''
from sklearn.metrics import mean_squared_error # 均方误差
from sklearn.metrics import mean_absolute_error # 平方绝对误差
from sklearn.metrics import r2_score # R square
'''
#from get_data import nn_seq
#from args import args_parser
#from util import train, test, get_best_parameters
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号 #有中文出现的情况，需要u'内容'
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
path = os.path.dirname(os.path.realpath(file ))
#filename=r'/data/森源张鼓匠数据2022-7至2023-6删除限电.csv'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def args_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=10, help='epochs')
parser.add_argument('--seq_len', type=int, default=16, help='seq len')
parser.add_argument('--hidden_size', type=int, default=16, help='hidden_size')
parser.add_argument('--input_size', type=int, default=27, help='input dimension')
parser.add_argument('--num_layers', type=int, default=1, help='num_layers')
parser.add_argument('--d_model', type=int, default=8, help='input dimension')
parser.add_argument('--output_size', type=int, default=16, help='output dimension')
parser.add_argument('--lr', type=float, default=0.0008, help='learning rate')
parser.add_argument('--batch_size', type=int, default=64, help='batch size')
parser.add_argument('--optimizer', type=str, default='adam', help='type of optimizer')
parser.add_argument('--device', default=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
parser.add_argument('--weight_decay', type=float, default=1e-4, help='weight decay')
parser.add_argument('--step_size', type=int, default=1, help='step size')
parser.add_argument('--gamma', type=float, default=0.25, help='gamma')
args = parser.parse_args()
return argsdef nn_seq(args):
seq_len, batch_size, pred_step_size= args.seq_len, args.batch_size, args.output_size
filename = '/data/data.csv'
filepath = os.path.dirname(os.path.realpath(file )) + filename
data = pd.read_csv(filepath, parse_dates=True, index_col=r'时间')
data = data.fillna('0')
data["cos预测风向"]=np.cos(data["预测风向(°)"].values.astype('float'))
data["sin预测风向"]=np.sin(data["预测风向(°)"].values.astype('float'))
data["实际功率"] = data["实际功率(MW)"]
#实际功率(MW),平均风速1(m/s),最大风速1(m/s),极大风速1(m/s),平均风向1(°),平均风速2(m/s),最大风速2(m/s),极大风速2(m/s),平均风向2(°),平均风速3(m/s),最大风速3(m/s),极大风速3(m/s),平均风向3(°),平均风速4(m/s),最大风速4(m/s),极大风速4(m/s),平均风向4(°),平均风速5(m/s),最大风速5(m/s),极大风速5(m/s),平均风向5(°),平均风速6(m/s),最大风速6(m/s),极大风速6(m/s),平均风向6(°),平均气温(℃),平均湿度(%),平均气压(hPa),平均空气密度(kg/m3),预测风速(m/s),预测风向(°),预测气温(℃),
data = data[["实际功率","平均风速1(m/s)","最大风速1(m/s)","极大风速1(m/s)","平均风向1(°)","平均风速2(m/s)","最大风速2(m/s)","极大风速2(m/s)","平均风向2(°)","平均风速3(m/s)","最大风速3(m/s)","极大风速3(m/s)","平均风向3(°)","平均风速4(m/s)","最大风速4(m/s)","极大风速4(m/s)","平均风向4(°)","平均风速5(m/s)","最大风速5(m/s)","极大风速5(m/s)","平均风向5(°)","预测风速(m/s)","预测风向(°)","预测气温(℃)"]]
#data = data[["实际功率","预测风速(m/s)","预测风向(°)","预测气温(℃)","cos预测风向","sin预测风向"]]#
train = data[:'2024-01-01 00:15:00']
val = data['2023-12-01 00:15:00':'2024-01-01 00:15:00']
test = data['2023-12-31 16:30:00':]
result=pd.DataFrame(columns=['实际功率', '真实值', '预测值'])
result['实际功率']=test['实际功率']['2024-01-01 00:15:00':]#[96+96-1:]
print(train.shape,val.shape,test.shape)
print(train.shape,test.shape)
num_nodes=24#3
'''
scaler1 = MinMaxScaler()
train.iloc[:,0] = scaler1.fit_transform(train.iloc[:,0].values.reshape([-1,1])).reshape([-1])
scaler2 = MinMaxScaler()
train.iloc[:,1] = scaler2.fit_transform(train.iloc[:,1].values.reshape([-1,1])).reshape([-1])
scaler3 = MinMaxScaler()
train.iloc[:,2] = scaler3.fit_transform(train.iloc[:,2].values.reshape([-1,1])).reshape([-1])
scaler4 = MinMaxScaler()
train.iloc[:,3] = scaler4.fit_transform(train.iloc[:,3].values.reshape([-1,1])).reshape([-1])
val.iloc[:,0] = scaler1.transform(val.iloc[:,0].values.reshape([-1,1])).reshape([-1])
val.iloc[:,1] = scaler2.transform(val.iloc[:,1].values.reshape([-1,1])).reshape([-1])
val.iloc[:,2] = scaler3.transform(val.iloc[:,2].values.reshape([-1,1])).reshape([-1])
val.iloc[:,3] = scaler4.transform(val.iloc[:,3].values.reshape([-1,1])).reshape([-1])
test.iloc[:,0] = scaler1.transform(test.iloc[:,0].values.reshape([-1,1])).reshape([-1])
test.iloc[:,1] = scaler2.transform(test.iloc[:,1].values.reshape([-1,1])).reshape([-1])
test.iloc[:,2] = scaler3.transform(test.iloc[:,2].values.reshape([-1,1])).reshape([-1])
test.iloc[:,3] = scaler4.transform(test.iloc[:,3].values.reshape([-1,1])).reshape([-1])
'''
def process(dataset,shuffle):
#观看过去时间窗口 过去多少天
past_history_size = 16
#预测未来值n天
future_target = 16
x = []
y = []
x1= []
x2= []
x3= []
dataset=dataset.values
for i in tqdm(range(len(dataset)-past_history_size-future_target+1)):
x.append(dataset[i:i+past_history_size,:])
x1.append(dataset[i+past_history_size:i+past_history_size+future_target,-1])
x2.append(dataset[i+past_history_size:i+past_history_size+future_target,-2])
x3.append(dataset[i+past_history_size:i+past_history_size+future_target,-3])
y.append(dataset[i+past_history_size:i+past_history_size+future_target,0])
x,x1,x2,x3 = np.array(x),np.array(x1),np.array(x2),np.array(x3)
x = x.reshape([-1,past_history_size,num_nodes])
x2=x2.reshape(-1,future_target,1)
x1=x1.reshape([-1,future_target,1])
x3=x3.reshape([-1,future_target,1])
x = np.array(x).astype(float)
x1 = np.array(x1).astype(float)
x2 = np.array(x2).astype(float)
x3 = np.array(x3).astype(float)
y=np.array(y).astype(float)
# 将x1, x2, x3拼接到x上
x = torch.tensor(x, dtype=torch.float)
x1 = torch.tensor(x1, dtype=torch.float)
x2 = torch.tensor(x2, dtype=torch.float)
x3 = torch.tensor(x3, dtype=torch.float) # 转换x3为Tensor
x = torch.cat((x, x1, x2, x3), dim=2) # 在最后一个维度上拼接x1, x2, x3
y=torch.tensor(y,dtype = torch.float)
#print('y'*100)
print(x.shape,y.shape)
loader = Data.DataLoader(dataset=Data.TensorDataset(x, y),batch_size=batch_size,
shuffle=shuffle, drop_last=False,num_workers=0)
return loader
def process_test(dataset):
#观看过去时间窗口 过去多少天
past_history_size = 16
#预测未来值n天
future_target = 16
x = []
y = []
x1= []
x2= []
x3= []
dataset=dataset.values
for i in tqdm(range(len(dataset)-past_history_size-future_target+1)):
x.append(dataset[i:i+past_history_size,:])
x1.append(dataset[i+past_history_size:i+past_history_size+future_target,-1])
x2.append(dataset[i+past_history_size:i+past_history_size+future_target,-2])
x3.append(dataset[i+past_history_size:i+past_history_size+future_target,-3])
y.append(dataset[i+past_history_size:i+past_history_size+future_target,0])
x,x1,x2,x3 = np.array(x),np.array(x1),np.array(x2),np.array(x3)
x = x.reshape([-1,past_history_size,num_nodes])
x2=x2.reshape(-1,future_target,1)
x1=x1.reshape([-1,future_target,1])
x3=x3.reshape([-1,future_target,1])
x = np.array(x).astype(float)
x1 = np.array(x1).astype(float)
x2 = np.array(x2).astype(float)
x3 = np.array(x3).astype(float)
y=np.array(y).astype(float)
# 将x1, x2, x3拼接到x上
x = torch.tensor(x, dtype=torch.float)
x1 = torch.tensor(x1, dtype=torch.float)
x2 = torch.tensor(x2, dtype=torch.float)
x3 = torch.tensor(x3, dtype=torch.float) # 转换x3为Tensor
x = torch.cat((x, x1, x2, x3), dim=2) # 在最后一个维度上拼接x1, x2, x3
y=torch.tensor(y,dtype = torch.float)
print(x.shape,y.shape)
return x,y
Dtr = process(dataset=train,shuffle=True)
Val = process(dataset=val,shuffle=True)
testX,testY = process_test(dataset=test)
#Y_test = torch.tensor(Y_test,dtype = torch.float)
#print(x_train.shape, y_train.shape,x_test.shape,y_test.shape)
return Dtr, Val, testX,testY,result#, scaler1class BiLSTM_Attention(nn.Module):
def init (self, args):
super(BiLSTM_Attention, self).init ()
self.args = args
#def init (self, input_size, hidden_size, output_size, num_layers=1):
self.hidden_size = args.hidden_size
self.num_layers = args.num_layers
self.lstm = nn.LSTM(args.input_size, args.hidden_size, args.num_layers, batch_first=True, bidirectional=True)
self.fc = nn.Linear(args.hidden_size * 2, args.output_size) # 双向LSTM，输出尺寸翻倍
self.attention_layer = nn.Sequential(
nn.Linear(args.hidden_size * 2, args.hidden_size), # 注意力层的全连接层
nn.Tanh(),
nn.Linear(args.hidden_size, 1, bias=False)
)
def attention(self, lstm_output):
# 将lstm_output看作是query和key，简化注意力机制
attn_weights = torch.matmul(lstm_output, lstm_output.transpose(1, 2)) # 点积注意力，不涉及permute
attn_weights = attn_weights / torch.sqrt(torch.tensor(self.hidden_size * 2).float()) # 缩放因子
attn_weights = torch.softmax(attn_weights, dim=-1)
# 加权求和
context_vector = torch.bmm(attn_weights, lstm_output)
return context_vector
def forward(self, x):
h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)
out, _ = self.lstm(x, (h0, c0)) # LSTM输出，形状为[batch_size, sequence_length, hidden_size * 2]
# 应用注意力机制
context_vector = self.attention(out) # 经过注意力机制处理后的向量，形状应为[batch_size, hidden_size * 2]
# 确保全连接层的输出维度与output_size匹配
# 注意：如果context_vector需要被进一步处理以适应多输出（例如，如果是多任务回归），这里可能需要调整
output = self.fc(context_vector) # 经过全连接层后的输出，形状应为[batch_size, output_size]
#print("outputs[:, 0, :]",output[:, 0, :].shape)
return output[:, 0, :]def get_val_loss(args, model, Val):
model.eval()
loss_function = nn.MSELoss().to(args.device)
val_loss = []
for (seq, label) in Val:
with torch.no_grad():
seq, label = seq.to(args.device), label.to(args.device)
label = label.to(args.device)
y_pred = model(seq)
loss = loss_function(y_pred, label)
val_loss.append(loss.item())
return np.mean(val_loss)def print_history(train_loss_,val_loss_):
plt.plot(train_loss_,color='Orange',label="train loss")
#print("history.history['loss']",len(history.history['loss']))
plt.plot(val_loss_,color='b',label="validation loss")
plt.title('train_validation loss')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.legend()
#plt.show()
plt.savefig(path+r'/loss/BiLSTM_Attention.png')
plt.close()
def train(args, Dtr, Val):
model = BiLSTM_Attention(args).to(args.device)
loss_function = nn.MSELoss().to(args.device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
print('training...')
epochs = args.epochs
min_epochs = 10
best_model = model#None
min_val_loss = 5
final_val_loss = []
train_loss_,val_loss_=[],[]
scheduler = StepLR(optimizer, step_size=args.step_size, gamma=args.gamma)
for epoch in range(epochs):
train_loss = []
for batch_idx, (seq, target) in enumerate(Dtr, 0):
seq, target = seq.to(args.device), target.to(args.device)
#target=target.reshape(-1,1)
optimizer.zero_grad()
y_pred = model(seq)
#print("y_pred",y_pred.shape)
#print("target",target.shape)
loss = loss_function(y_pred, target)
train_loss.append(loss.item())
loss.backward()
optimizer.step()
scheduler.step()
validation
val_loss = get_val_loss(args, model, Val)
if epoch + 1 >= min_epochs and val_loss < min_val_loss:
min_val_loss = val_loss
best_model = copy.deepcopy(model)
print('epoch {:03d} train_loss {:.8f} val_loss {:.8f}'.format(epoch, np.mean(train_loss), val_loss))
final_val_loss.append(val_loss)
model.train()
train_loss_.append(np.mean(train_loss))
val_loss_.append(val_loss )
print_history(train_loss_,val_loss_)
#state = {'model': best_model.state_dict(), 'optimizer': optimizer.state_dict()}
#torch.save(state,PATH)
#torch.save(best_model,r'D:\我的坚果云\workworkwork\202308work\光伏功率超短期预测\model\BiLSTM_Attention.pth')
#torch.save(best_model, path+r'\model\BiLSTM_Attention.pth')
torch.save(best_model,path+r'/model/BiLSTM_Attention.pth')
#torch.save(best_model, path+r'\model\BiLSTM_Attention.pth')
return np.mean(final_val_loss)@torch.no_grad()
def test(X_test1,ys,path,result,Cap):#,scaler1):# scaler,
print('loading models...')
X_test1 = torch.tensor(X_test1,dtype = torch.float).to(device)
print(X_test1.shape)
#BiLSTM_Attention
model=torch.load(path+r'/model/BiLSTM_Attention.pth').to(device)#, map_location=torch.device('cpu'))
#model.eval()
print('predicting...')
preds = model(X_test1)#,X_test2)
preds = preds.detach().cpu().numpy()
ys = ys.numpy()
#print(ys.shape,preds.shape)
#y = scaler1.inverse_transform(ys)#.T
#pred = scaler1.inverse_transform(preds)#.T
y,pred=ys[:,-1],preds[:,-1]
for j in range(len(pred)):
pred[j] = np.round(pred[j], 3)
if pred[j] < 0:
pred[j] = float(0)
if pred[j]>Cap:
pred[j]=Cap
print(y.shape,pred.shape)
#print(type(y),type(pred))
print('mse:', get_mse(y, pred))
print('rmse:', get_rmse(y, pred))
print('mae:', get_mae(y, pred))
print('mape:', get_mape(y, pred))
print('r2score',get_r2(y,pred))
result['真实值']=y#[2192-2014:]
result['预测值']=pred#[2192-2014:]
plot_test(y, pred)
result.to_csv(path+r'/result/BiLSTM_Attention.csv', sep=',')
mse=get_mse(y, pred)
rmse=get_rmse(y, pred)
mae=get_mae(y, pred)
mape= get_mape(y, pred)
r2score=get_r2(y,pred)
df = {"mse": mse, "rmse": rmse,"mae": mae, "mape": mape,"r2score":r2score}
df = pd.DataFrame(list(df.items()))
df.to_csv(path + '/result_score/BiLSTM_Attention-result_score.csv')
@torch.no_grad()
def test16(X_test1,ys,path,result,Cap):#,scaler1):# scaler,
print('loading models...')
X_test1 = torch.tensor(X_test1,dtype = torch.float).to(device)
print(X_test1.shape)
model=torch.load(path+r'/model/BiLSTM_Attention.pth').to(device)#, map_location=torch.device('cpu'))
#model.eval()
print('predicting...')
preds = model(X_test1)#,X_test2)
preds = preds.detach().cpu().numpy()
ys = ys.numpy()
#print(ys.shape,preds.shape)
#y = scaler1.inverse_transform(ys)#.T
#pred = scaler1.inverse_transform(preds)#.T
y,pred=ys[:,-1],preds[:,-1]
print(y.shape,pred.shape)
#print(type(y),type(pred))
print('mse:', get_mse(y, pred))
print('rmse:', get_rmse(y, pred))
print('mae:', get_mae(y, pred))
print('mape:', get_mape(y, pred))
print('r2score',get_r2(y,pred))
result['真实值']=y#[2192-2014:]
result['预测值']=pred#[2192-2014:]
plot_test(y, pred)
def correction(jj):
for j in range(len(preds[:,jj])):
preds[:,jj][j] = np.round(preds[:,jj][j], 3)
if preds[:,jj][j] < 0:
preds[:,jj][j] = float(0)
if preds[:,jj][j]>Cap:
preds[:,jj][j]=Cap
for j in range(16):
correction(j)
result['真实值']=y
for i in range(16):
result['预测值'+str(i)]=preds[:,i]
result.to_csv(path+r'/result/BiLSTM_Attention16个点.csv', sep=',')
mse=get_mse(y, pred)
rmse=get_rmse(y, pred)
mae=get_mae(y, pred)
mape= get_mape(y, pred)
r2score=get_r2(y,pred)
df = {"mse": mse, "rmse": rmse,"mae": mae, "mape": mape,"r2score":r2score}
df = pd.DataFrame(list(df.items()))
df.to_csv(path + '/result_score/BiLSTM_Attention取16个点最后一个-result_score.csv')def plot_test(y, pred):
plot
plt.plot(y, color='blue', label='true value')
plt.plot(pred, color='red', label='pred value')
plt.title('BiLSTM_Attention的预测结果')
#plt.grid(True)
plt.legend(loc='upper center', ncol=6)
#plt.show()
plt.savefig(path+r'/pictures/BiLSTM_Attention.png')
def get_mape(y_true, y_pred):
"""
计算平均绝对百分比误差 (Mean Absolute Percentage Error)
"""
防止除以零和负数开根号的情况
y_true = np.where(y_true == 0, 1e-6, y_true) # 对于0值，用一个极小正数替代以避免除法错误
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
def get_r2(y_true, y_pred):
"""
计算决定系数 R²
"""
rss = np.sum((y_true - y_pred) ** 2) # 剩余平方和
tss = np.sum((y_true - np.mean(y_true)) ** 2) # 总平方和
return 1 - (rss / tss)
def get_mae(y_true, y_pred):
"""
计算平均绝对误差 (Mean Absolute Error)
"""
return np.mean(np.abs(y_true - y_pred))
def get_mse(y_true, y_pred):
"""
计算均方误差 (Mean Squared Error)
"""
return np.mean((y_true - y_pred) ** 2)
def get_rmse(y, pred):
return np.sqrt(get_mse(y, pred))
def get_best_parameters(args, Dtr, Val):
def objective(trial):
model = BiLSTM_Attention(args).to(args.device)
loss_function = nn.MSELoss().to(args.device)
optimizer = trial.suggest_categorical('optimizer',
torch.optim.SGD, torch.optim.RMSprop, torch.optim.Adam\])( model.parameters(), lr=trial.suggest_loguniform('lr', 5e-4, 1e-2)) print('training...') epochs = 10 val_loss = 0 for epoch in range(epochs): train_loss = \[
for batch_idx, (seq, target) in enumerate(Dtr, 0):
seq, target = seq.to(args.device), target.to(args.device)
optimizer.zero_grad()
y_pred = model(seq)
loss = loss_function(y_pred, target)
train_loss.append(loss.item())
loss.backward()
optimizer.step()
validation
val_loss = get_val_loss(args, model, Val)
print('epoch {:03d} train_loss {:.8f} val_loss {:.8f}'.format(epoch, np.mean(train_loss), val_loss))
model.train()
return val_loss
sampler = optuna.samplers.TPESampler()
study = optuna.create_study(sampler=sampler, direction='minimize')
study.optimize(func=objective, n_trials=5)
pruned_trials = study.get_trials(deepcopy=False,
states=tuple([optuna.trial.TrialState.PRUNED]))
complete_trials = study.get_trials(deepcopy=False,
states=tuple([optuna.trial.TrialState.COMPLETE]))
best_trial = study.best_trial
print('val_loss = ', best_trial.value)
for key, value in best_trial.params.items():
print("{}: {}".format(key, value))def main():
Cap=300
args = args_parser()
Dtr, Val, testX,testY,result= nn_seq(args)
get_best_parameters(args, Dtr, Val)
import time
T1 = time.time()
train(args, Dtr, Val)#, PATH
print('train用时',time.time()-T1)
T2=time.time()
test( testX,testY,path,result,Cap)
test16( testX,testY,path,result,Cap)#, scaler1)
print('test用时',time.time()-T2)
if name == 'main ':
main()