import torch
import torch.nn as nn
import numpy as np
import time
import math
from matplotlib import pyplot
import matplotlib.pyplot as plt;
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
import tensorflow as tf
from math import sqrt
from sklearn import metrics
torch.manual_seed(0)
np.random.seed(0)
import warnings
warnings.filterwarnings('ignore')
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEncoding, self).__init__()
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
#pe.requires_grad = False
self.register_buffer('pe', pe)
def forward(self, x):
return x + self.pe[:x.size(0), :]
#构建transformer模型
class TransAm(nn.Module):
def __init__(self,feature_size=250,num_layers=1,dropout=0.1):
super(TransAm, self).__init__()
self.model_type = 'Transformer'
self.src_mask = None
self.pos_encoder = PositionalEncoding(feature_size)
self.encoder_layer = nn.TransformerEncoderLayer(d_model=feature_size, nhead=10, dropout=dropout)
self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)
self.decoder = nn.Linear(feature_size,1)
self.init_weights()
def init_weights(self):
initrange = 0.1
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
def forward(self,src):
if self.src_mask is None or self.src_mask.size(0) != len(src):
device = src.device
mask = self._generate_square_subsequent_mask(len(src)).to(device)
self.src_mask = mask
src = self.pos_encoder(src)
output = self.transformer_encoder(src,self.src_mask)#, self.src_mask)
output = self.decoder(output)
return output
def _generate_square_subsequent_mask(self, sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def get_batch(source, i,batch_size):
seq_len = min(batch_size, len(source) - 1 - i)
data = source[i:i+seq_len]
input = torch.stack(torch.stack([item[0] for item in data]).chunk(input_window,1)) # 1 is feature size
target = torch.stack(torch.stack([item[1] for item in data]).chunk(input_window,1))
return input, target
#训练模型
def train(train_data):
model.train() # Turn on the train mode \o/
total_loss = 0.
start_time = time.time()
for batch, i in enumerate(range(0, len(train_data) - 1, batch_size)):
data, targets = get_batch(train_data, i,batch_size)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, targets)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.7)
optimizer.step()
total_loss += loss.item()
log_interval = int(len(train_data) / batch_size / 5)
if batch % log_interval == 0 and batch > 0:
cur_loss = total_loss / log_interval
elapsed = time.time() - start_time
print('| epoch {:3d} | {:5d}/{:5d} batches | '
'lr {:02.6f} | {:5.2f} ms | '
'loss {:5.5f} | ppl {:8.2f}'.format(
epoch, batch, len(train_data) // batch_size, scheduler.get_lr()[0],
elapsed * 1000 / log_interval,
cur_loss, math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
#预测
def plot_and_loss(eval_model, data_source,epoch):
eval_model.eval()
total_loss = 0.
test_result = torch.Tensor(0)
truth = torch.Tensor(0)
with torch.no_grad():
for i in range(0, len(data_source) - 1):
data, target = get_batch(data_source, i,1)
output = eval_model(data)
total_loss += criterion(output, target).item()
test_result = torch.cat((test_result, output[-1].view(-1).cpu()), 0)
truth = torch.cat((truth, target[-1].view(-1).cpu()), 0)
#test_result = test_result.cpu().numpy() -> no need to detach stuff..
len(test_result)
pyplot.plot(test_result,color="red")
pyplot.plot(truth[:500],color="blue")
# pyplot.plot(test_result-truth,color="green")
pyplot.grid(True, which='both')
pyplot.axhline(y=0, color='k')
# pyplot.savefig('graph/transformer-epoch%d.png'%epoch)
plt.show()
# pyplot.close()
real=truth.tolist()
pre=test_result.tolist()
return total_loss / i,real,pre
# predict the next n steps based on the input data
# def predict_future(eval_model, data_source,steps):
# eval_model.eval()
# total_loss = 0.
# test_result = torch.Tensor(0)
# truth = torch.Tensor(0)
# data, _ = get_batch(data_source, 0,1)
# with torch.no_grad():
# for i in range(0, steps):
# output = eval_model(data[-input_window:])
# data = torch.cat((data, output[-1:]))
# data = data.cpu().view(-1)
# # print(data)
# # I used this plot to visualize if the model pics up any long therm struccture within the data.
# pyplot.plot(data,color="red")
# pyplot.plot(data[:input_window],color="blue")
# pyplot.grid(True, which='both')
# pyplot.axhline(y=0, color='k')
# # pyplot.savefig('graph/transformer-future%d.png'%steps)
# # pyplot.close()
# plt.show()
def evaluate(eval_model, data_source):
eval_model.eval() # Turn on the evaluation mode
total_loss = 0.
eval_batch_size = 1000
with torch.no_grad():
for i in range(0, len(data_source) - 1, eval_batch_size):
data, targets = get_batch(data_source, i,eval_batch_size)
output = eval_model(data)
total_loss += len(data[0])* criterion(output, targets).cpu().item()
return total_loss / len(data_source)
input_window = 30 # number of input steps
output_window = 1 # number of prediction steps, in this model its fixed to one
batch_size = 10
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# if window is 100 and prediction step is 1
# in -> [0..99]
# target -> [1..100]
def create_inout_sequences(input_data, tw):
inout_seq = []
L = len(input_data)
for i in range(L-tw):
train_seq = input_data[i:i+tw]
train_label = input_data[i+output_window:i+tw+output_window]
inout_seq.append((train_seq ,train_label))
return torch.FloatTensor(inout_seq)
#构建数据
def get_data():
# construct a littel toy dataset
# time = np.arange(0, 200, 0.1)
# print("输入数据time：")
# print(time.shape)
# amplitude = np.sin(time) + np.sin(time*0.05) +np.sin(time*0.12) *np.random.normal(-0.2, 0.2, len(time))
# print("输入数据amplitude1：")
# print(amplitude.shape)
# print(amplitude)
#loading data from a file
from pandas import read_csv
dataset= pd.read_excel(r"D:\alldata\pythonfiles\九寨沟\数据\九寨沟数据.xlsx")
dataset.drop('日期',axis=1, inplace=True)
amplitude=dataset.values
print(amplitude)
# looks like normalizing input values curtial for the model
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(-1, 1))
#amplitude = scaler.fit_transform(series.to_numpy().reshape(-1, 1)).reshape(-1)
amplitude = scaler.fit_transform(amplitude.reshape(-1, 1)).reshape(-1)
print("输入数据amplitude2：")
print(amplitude.shape)
# sampels = 2600
sampels = 1246
train_data = amplitude[:sampels]
test_data = amplitude[sampels:]
test_data2 = amplitude[sampels:]
print("输入数据train_data1：")
print(train_data.shape)
print("输入数据test_data1：")
print(test_data.shape)
# convert our train data into a pytorch train tensor
#train_tensor = torch.FloatTensor(train_data).view(-1)
# todo: add comment..
train_sequence = create_inout_sequences(train_data,input_window)
train_sequence = train_sequence[:-output_window] #todo: fix hack? -> din't think this through, looks like the last n sequences are to short, so I just remove them. Hackety Hack..
#test_data = torch.FloatTensor(test_data).view(-1)
test_data = create_inout_sequences(test_data,input_window)
test_data = test_data[:-output_window] #todo: fix hack?
print("输入数据train_sequence：")
print(train_sequence.shape)
print("输入数据test_data2：")
print(test_data.shape)
return train_sequence.to(device),test_data.to(device),test_data2,scaler
model = TransAm().to(device)
criterion = nn.MSELoss()
lr = 0.005
#optimizer = torch.optim.SGD(model.parameters(), lr=lr)
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
train_data, val_data,val_data2,scaler= get_data()
best_val_loss = float("inf")
epochs = 100 # The number of epochs
best_model = None
for epoch in range(1, epochs + 1):
epoch_start_time = time.time()
train(train_data)
#val_loss,pre,real = plot_and_loss(model, val_data,epoch)
if(epoch % 100 is 0):
val_loss,pre,real = plot_and_loss(model, val_data,epoch)
pre=np.array(pre).reshape(1,len(pre))
pre= scaler.inverse_transform(pre).flatten()
print(pre)
real=np.array(real).reshape(1,len(real))
real= scaler.inverse_transform(real).flatten()
print(real)
plt.plot(real)
plt.plot(pre)
plt.ylabel('finalpre')
plt.xlabel('y')
plt.legend(['finalpre','y'])
plt.show()
MSE = mean_squared_error(real,pre)
RMSE = math.sqrt(MSE)
MAE=mean_absolute_error(real, pre)
MAPE = metrics.mean_absolute_percentage_error(real,pre)
print("rmse :",RMSE)
print("mae :", MAE)
print("mape :", MAPE)
# predict_future(model, val_data,200)
else:
val_loss = evaluate(model, val_data)
print('-' * 89)
print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.5f} | valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
val_loss, math.exp(val_loss)))
print('-' * 89)
if val_loss < best_val_loss:
best_val_loss = val_loss
best_model = model
scheduler.step()
