前言

今天我們要使用 RNN 類別中的 LSTM 來實做一個股價預測的例子，這種預測未來值的 case 也叫做 regression

Regression

Code

我們先放上 code 來~

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import torch.nn as nn
import torch
from torch.autograd import Variable

# Globals

INPUT_SIZE = 60
HIDDEN_SIZE = 64
NUM_LAYERS = 2
OUTPUT_SIZE = 1

# Hyper parameters

learning_rate = 0.001
num_epochs = 50

# Importing the training set
dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
training_set = dataset_train.iloc[:, 1:2].values

# Feature Scaling
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)

# Creating a data structure with 60 timesteps and 1 output
X_train = []
y_train = []
for i in range(INPUT_SIZE, 1258):
    X_train.append(training_set_scaled[i-INPUT_SIZE:i, 0])
    y_train.append(training_set_scaled[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)

# Reshaping
X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))

class RNN(nn.Module):
    def __init__(self, i_size, h_size, n_layers, o_size):
        super(RNN, self).__init__()

        self.rnn = nn.LSTM(
            input_size=i_size,
            hidden_size=h_size,
            num_layers=n_layers
        )
        self.out = nn.Linear(h_size, o_size)

    def forward(self, x, h_state):
        r_out, hidden_state = self.rnn(x, h_state)
        
        hidden_size = hidden_state[-1].size(-1)
        r_out = r_out.view(-1, hidden_size)
        outs = self.out(r_out)

        return outs, hidden_state

rnn = RNN(INPUT_SIZE, HIDDEN_SIZE, NUM_LAYERS, OUTPUT_SIZE)

optimiser = torch.optim.Adam(rnn.parameters(), lr=learning_rate)
criterion = nn.MSELoss()

hidden_state = None

for epoch in range(num_epochs):
    inputs = Variable(torch.from_numpy(X_train).float())
    labels = Variable(torch.from_numpy(y_train).float())

    output, hidden_state = rnn(inputs, hidden_state) 

    loss = criterion(output.view(-1), labels)
    optimiser.zero_grad()
    loss.backward(retain_graph=True)                     # back propagation
    optimiser.step()                                     # update the parameters
    
    print('epoch {}, loss {}'.format(epoch,loss.item()))

# Getting the real stock price of 2017
dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')
real_stock_price = dataset_test.iloc[:, 1:2].values

# Getting the predicted stock price of 2017
dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - INPUT_SIZE:].values
inputs = inputs.reshape(-1,1)
inputs = sc.transform(inputs)
X_test = []
for i in range(INPUT_SIZE, 80):
    X_test.append(inputs[i-INPUT_SIZE:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))

X_train_X_test = np.concatenate((X_train, X_test),axis=0)
hidden_state = None
test_inputs = Variable(torch.from_numpy(X_train_X_test).float())
predicted_stock_price, b = rnn(test_inputs, hidden_state)
predicted_stock_price = np.reshape(predicted_stock_price.detach().numpy(), (test_inputs.shape[0], 1))
predicted_stock_price = sc.inverse_transform(predicted_stock_price)

real_stock_price_all = np.concatenate((training_set[INPUT_SIZE:], real_stock_price))

# Visualising the results
plt.figure(1, figsize=(12, 5))
plt.plot(real_stock_price_all, color = 'red', label = 'Real')
plt.plot(predicted_stock_price, color = 'blue', label = 'Pred')
plt.title('Google Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('Google Stock Price')
plt.legend()
plt.show()

參考資料

1.https://github.com/thundercomb/pytorch-stock-predictor-rnn/blob/master/pytorch-stock-predictor-rnn.py