Pytoch temperature prediction

pytorch- Temperature prediction

dir = r'E:\PyTorch\02\02.2020 Deep learning -PyTorch actual combat \ Code + Information \ Neural network combat classification and regression task \temps.csv'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import  torch

Data = pd.read_csv(dir)

print(Data)
print(Data.head())
del Data['friend']
print(Data)
print(' Data dimension ：',Data.shape)

# Processing time data 
import datetime
years = Data['year']
months = Data['month']
days =  Data['day']

#datetime Format 
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year,month,day in zip(years,months,days)]
dates = [datetime.datetime.strptime(date,'%Y-%m-%d') for date in dates]
print(dates[:5])

# mapping 
# Specify the default style 
# plt.style.use('fivethirtyeight')
# plt.figure(dpi=400)
# #plt.subplot(2,2,1)
# plt.plot(dates,Data['actual'])
# plt.xlabel('')
# plt.ylabel('Temperature')
# plt.title('Max Temp')
# plt.show()
#
# plt.figure(dpi=400)
# #plt.subplot(2,2,2)
# plt.plot(dates,Data['temp_1'])
# plt.xlabel('')
# plt.ylabel('Temperature')
# plt.title('Previous Max Temp')
# plt.show()
#
# plt.figure(dpi=400)
# #plt.subplot(2,2,3)
# plt.plot(dates,Data['temp_2'])
# plt.xlabel('')
# plt.ylabel('Temperature')
# plt.title('Two Days Prior Max Temp')
#
# plt.show()

# week  Column   A special   yes   String type 
# Hot coding alone 
Data = pd.get_dummies(Data)
print(Data.head())


# label 
labels = np.array(Data['actual'])

Data = Data.drop('actual',axis=1)

Data_columns_name = list(Data.columns)

Data = np.array(Data)

print(Data.shape)

from sklearn import preprocessing
input_Data = preprocessing.StandardScaler().fit_transform(Data)

# Build the model 
x = torch.tensor(input_Data,dtype=float)
y = torch.tensor(labels,dtype=float)
""" # Customize the calculation method to build the model  # Weight parameter initialization  weights = torch.randn((13,128),dtype=float,requires_grad=True) biases = torch.randn(128,dtype=float,requires_grad=True) weights1 = torch.randn((128,1),dtype=float,requires_grad=True) biases1 = torch.randn(1,dtype=float,requires_grad=True) lr = 0.001 losses = [] for i in range(1000): hidden = x.mm(weights) + biases hidden = torch.relu(hidden) predictions = hidden.mm(weights1) + biases1 loss = torch.mean((predictions-y)**2) losses.append(loss.data.numpy()) # Print loss value  if i % 100 == 0: print('loss:',loss) loss.backward() # Update parameters  weights.data.add_(-lr*weights.grad.data) biases.data.add_(-lr*biases.grad.data) weights1.data.add_(-lr*weights1.grad.data) biases1.data.add_(-lr*biases1.grad.data) # Every iteration should clear the gradient  weights.grad.data.zero_() biases.grad.data.zero_() weights1.grad.data.zero_() biases1.grad.data.zero_() """

# Concise network model 
input_size = input_Data.shape[1]
hidden_size = 128
output_size = 1
batch_size = 16

my_nn = torch.nn.Sequential(
    torch.nn.Linear(input_size,hidden_size),
    torch.nn.Sigmoid(),
    torch.nn.Linear(hidden_size,output_size)
)

cost = torch.nn.MSELoss(reduction='mean')
optimizer= torch.optim.Adam(my_nn.parameters(),lr=0.001)

#training
losses = []
for i in range(1000):
    batch_loss = []
    for start in range(0,len(input_Data),batch_size):
        end = start + batch_size if start + batch_size < len(input_Data) else len(input_Data)
        xx = torch.tensor(input_Data[start:end],dtype=torch.float,requires_grad=True)
        yy = torch.tensor(labels[start:end],dtype=torch.float,requires_grad=True)
        prediction = my_nn(xx)
        loss = cost(prediction,yy)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        batch_loss.append(loss.data.numpy())

    if i % 100 ==0:
        losses.append(np.mean(batch_loss))
        print(i,np.mean(batch_loss))


# Predict training results 
x = torch.tensor(input_Data,dtype=torch.float)
predict = my_nn(x).data.numpy()

# Convert date format 
dates = [str(int(year)) + '-' +str(int(month)) + '-' + str(int(day)) for year,month,day in zip(years,months,days)]
dates = [datetime.datetime.strptime(date,'%Y-%m-%d') for date in dates]

# Create a table to store the date and the corresponding tag value 
true_data = pd.DataFrame(data={
    'date':dates,'actual':labels})

# Then create an incoming date and the predicted value of the corresponding model 
months =Data[:,Data_columns_name.index('month')]
days = Data[:,Data_columns_name.index('day')]
years = Data[:,Data_columns_name.index('year')]

test_dates = [str(int(year)) + '-' +str(int(month)) + '-' + str(int(day)) for year,month,day in zip(years,months,days)]
test_dates = [datetime.datetime.strptime(date,'%Y-%m-%d') for date in test_dates]

# Create a table to store the date and the corresponding tag value 
predict_data = pd.DataFrame(data={
    'date':test_dates,'prediction':predict.reshape(-1)})
plt.figure(dpi=400)
#True
plt.plot(true_data['date'],true_data['actual'],'b-',label='actual')

#predict
plt.plot(predict_data['date'],predict_data['prediction'],'ro',label='prediction')
plt.xticks()
plt.legend()
plt.xlabel('Date')
plt.ylabel('Maximum Temperature (F)')
plt.title('Actual and Predicted Values')
plt.show()