lstm_regression_multivariate.py

# -*- coding: utf-8 -*-
"""
Created on Fri Aug  2 00:02:53 2019

@author: tanma
"""
import math
from matplotlib import pyplot
from pandas import read_csv, DataFrame, concat
from datetime import datetime
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from sklearn.metrics import mean_squared_error
from numpy import concatenate
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM

def parse(x):
	return datetime.strptime(x, '%Y %m %d %H')
dataset = read_csv('raw.csv',  parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
dataset.drop('No', axis=1, inplace=True)

dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']
dataset.index.name = 'date'

dataset['pollution'].fillna(0, inplace=True)

dataset = dataset[24:]

dataset.to_csv('pollution.csv')

def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
	n_vars = 1 if type(data) is list else data.shape[1]
	df = DataFrame(data)
	cols, names = list(), list()

	for i in range(n_in, 0, -1):
		cols.append(df.shift(i))
		names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]

	for i in range(0, n_out):
		cols.append(df.shift(-i))
		if i == 0:
			names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
		else:
			names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]

	agg = concat(cols, axis=1)
	agg.columns = names

	if dropnan:
		agg.dropna(inplace=True)
	return agg

dataset = read_csv('pollution.csv', header=0, index_col=0)
values = dataset.values

encoder = LabelEncoder()
values[:,4] = encoder.fit_transform(values[:,4])

values = values.astype('float32')

scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)

reframed = series_to_supervised(scaled, 1, 1)

reframed.drop(reframed.columns[[9,10,11,12,13,14,15]], axis=1, inplace=True)

values = reframed.values
n_train_hours = 365 * 24
train = values[:n_train_hours, :]
test = values[n_train_hours:, :]

train_X, train_y = train[:, :-1], train[:, -1]
test_X, test_y = test[:, :-1], test[:, -1]

train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))

model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')

history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)

pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.legend()
pyplot.show()

yhat = model.predict(test_X)
test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))

inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:,0]

test_y = test_y.reshape((len(test_y), 1))
inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:,0]

rmse = math.sqrt(mean_squared_error(inv_y, inv_yhat))
print('Test RMSE: %.3f' % rmse)