Created
January 3, 2018 06:00
-
-
Save tomthetrainer/e36ac9a226c1bcd55a0388f98b76973c to your computer and use it in GitHub Desktop.
MultiVariate Time Series Keras => DL4J
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
from math import sqrt | |
from numpy import concatenate | |
#from matplotlib import pyplot | |
from pandas import read_csv | |
from pandas import DataFrame | |
from pandas import concat | |
from sklearn.preprocessing import MinMaxScaler | |
from sklearn.preprocessing import LabelEncoder | |
from sklearn.metrics import mean_squared_error | |
from keras.models import Sequential | |
from keras.layers import Dense | |
from keras.layers import LSTM | |
# convert series to supervised learning | |
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() | |
# input sequence (t-n, ... t-1) | |
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)] | |
# forecast sequence (t, t+1, ... t+n) | |
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)] | |
# put it all together | |
agg = concat(cols, axis=1) | |
agg.columns = names | |
# drop rows with NaN values | |
if dropnan: | |
agg.dropna(inplace=True) | |
return agg | |
# load dataset | |
dataset = read_csv('pollution.csv', header=0, index_col=0) | |
values = dataset.values | |
# integer encode direction | |
encoder = LabelEncoder() | |
values[:,4] = encoder.fit_transform(values[:,4]) | |
# ensure all data is float | |
values = values.astype('float32') | |
# normalize features | |
scaler = MinMaxScaler(feature_range=(0, 1)) | |
scaled = scaler.fit_transform(values) | |
# frame as supervised learning | |
reframed = series_to_supervised(scaled, 1, 1) | |
# drop columns we don't want to predict | |
reframed.drop(reframed.columns[[9,10,11,12,13,14,15]], axis=1, inplace=True) | |
print(reframed.head()) | |
# split into train and test sets | |
values = reframed.values | |
n_train_hours = 365 * 24 | |
train = values[:n_train_hours, :] | |
test = values[n_train_hours:, :] | |
# split into input and outputs | |
train_X, train_y = train[:, :-1], train[:, -1] | |
test_X, test_y = test[:, :-1], test[:, -1] | |
# reshape input to be 3D [samples, timesteps, features] | |
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])) | |
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape) | |
# design network | |
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') | |
# fit network | |
history = model.fit(train_X, train_y, nb_epoch=5, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False) | |
# plot history | |
#pyplot.plot(history.history['loss'], label='train') | |
#pyplot.plot(history.history['val_loss'], label='test') | |
#pyplot.legend() | |
#pyplot.show() | |
# make a prediction | |
yhat = model.predict(test_X) | |
test_X = test_X.reshape((test_X.shape[0], test_X.shape[2])) | |
# invert scaling for forecast | |
inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1) | |
inv_yhat = scaler.inverse_transform(inv_yhat) | |
inv_yhat = inv_yhat[:,0] | |
# invert scaling for actual | |
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] | |
# calculate RMSE | |
rmse = sqrt(mean_squared_error(inv_y, inv_yhat)) | |
print('Test RMSE: %.3f' % rmse) | |
#model.save('pollution.h5') | |
print('#####INPUT#######') | |
print(test_X) | |
print('#####OUTPUT#######') | |
print(yhat) | |
print('#####Scaled Input #######') | |
print(inv_y) | |
print('#####Scaled Forecast #######') | |
print(inv_yhat) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Hello I hope you're well. I'm struggling with MinMaxScaler method i wanna to implement it on tweets but i don't how. Could you help me please?