lstm.py

"""
========================
LSTM forecasting
========================

LSTM sample

This model uses a time series of neurological data, trains a LSTM deep neural network to try to predict
t+1 value based on t sample.  The dimension of the input and output is one.

* https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
* https://arxiv.org/abs/1507.06947

"""
print(__doc__)

# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
signals = pd.read_csv('data/blinking.dat', delimiter=' ', usecols=[2], names = ['timestamp','counter','eeg','attention','meditation','blinking'])
signals = signals.astype('float32')


# %%
print('The Lag plot can be used to understand the correlation between consecutive time points.')
from matplotlib import pyplot
from pandas.plotting import lag_plot
lag_plot(signals)
pyplot.show()

# %%
import numpy
import matplotlib.pyplot as plt
import pandas
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error

# fix random seed for reproducibility
numpy.random.seed(7)


# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(signals)

# %%
# split into train and test sets
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
print(len(train), len(test))
# %%

# convert an array of values into a dataset matrix
# If loop_back is 1, we are using t to predict the value t+1
def create_dataset(dataset, look_back=1):
	dataX, dataY = [], []
	for i in range(len(dataset)-look_back-1):
		a = dataset[i:(i+look_back), 0]
		dataX.append(a)
		dataY.append(dataset[i + look_back, 0])
	return numpy.array(dataX), numpy.array(dataY)

# %%
# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# %%
# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))


# %%
print('This is a very basic deep neural network with input shape 1, 4 neurons on the first layer, and one output layer.')
print('The fitness function is based on MSE, and the optimizer is Adam.')
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
hist = model.fit(trainX, trainY, epochs=10, batch_size=1, verbose=2)


# %%
plt.plot(hist.history['loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train'], loc='upper right')
plt.show()


# %%
# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# invert predictions
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))

# %%
# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot+200)
plt.show()