99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
# Recurrent Neural Network
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# Part 1 - Data Preprocessing
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# Importing the libraries
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# Importing the training set
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dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
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training_set = dataset_train.iloc[:, 1:2].values
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# Feature Scaling
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from sklearn.preprocessing import MinMaxScaler
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sc = MinMaxScaler(feature_range = (0, 1))
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training_set_scaled = sc.fit_transform(training_set)
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# Creating a data structure with 60 timesteps and 1 output
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X_train = []
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y_train = []
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for i in range(60, 1258):
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X_train.append(training_set_scaled[i-60:i, 0])
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y_train.append(training_set_scaled[i, 0])
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X_train, y_train = np.array(X_train), np.array(y_train)
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# Reshaping
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X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
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# Part 2 - Building the RNN
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# Importing the Keras libraries and packages
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from keras.models import Sequential
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from keras.layers import Dense
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from keras.layers import LSTM
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from keras.layers import Dropout
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# Initialising the RNN
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regressor = Sequential()
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# Adding the first LSTM layer and some Dropout regularisation
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regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
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regressor.add(Dropout(0.2))
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# Adding a second LSTM layer and some Dropout regularisation
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regressor.add(LSTM(units = 50, return_sequences = True))
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regressor.add(Dropout(0.2))
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# Adding a third LSTM layer and some Dropout regularisation
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regressor.add(LSTM(units = 50, return_sequences = True))
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regressor.add(Dropout(0.2))
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# Adding a fourth LSTM layer and some Dropout regularisation
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regressor.add(LSTM(units = 50))
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regressor.add(Dropout(0.2))
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# Adding the output layer
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regressor.add(Dense(units = 1))
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# Compiling the RNN
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regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')
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# Fitting the RNN to the Training set
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regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)
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# Part 3 - Making the predictions and visualising the results
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# Getting the real stock price of 2017
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dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')
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real_stock_price = dataset_test.iloc[:, 1:2].values
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# Getting the predicted stock price of 2017
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dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)
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inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
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inputs = inputs.reshape(-1,1)
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inputs = sc.transform(inputs)
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X_test = []
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for i in range(60, 80):
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X_test.append(inputs[i-60:i, 0])
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X_test = np.array(X_test)
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X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
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predicted_stock_price = regressor.predict(X_test)
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predicted_stock_price = sc.inverse_transform(predicted_stock_price)
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# Visualising the results
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plt.plot(real_stock_price, color = 'red', label = 'Real Google Stock Price')
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plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Google Stock Price')
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plt.title('Google Stock Price Prediction')
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plt.xlabel('Time')
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plt.ylabel('Google Stock Price')
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plt.legend()
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plt.show()
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