In [6]:
from pandas import read_csv
from datetime import datetime
# load data
def parse(x):
return datetime.strptime(x, '%Y %m %d %H')
dataset = read_csv('../data/PRSA_data_2010.1.1-2014.12.31.csv', parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
dataset.drop('No', axis=1, inplace=True)
# manually specify column names
dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']
dataset.index.name = 'date'
# mark all NA values with 0
dataset['pollution'].fillna(0, inplace=True)
# drop the first 24 hours
dataset = dataset[24:]
# summarize first 5 rows
print(dataset.head(5))
# save to file
dataset.to_csv('../data/pollution.csv')
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from pandas import read_csv
from matplotlib import pyplot
# load dataset
dataset = read_csv('../data/pollution.csv', header=0, index_col=0)
values = dataset.values
# specify columns to plot
groups = [0, 1, 2, 3, 5, 6, 7]
i = 1
# plot each column
pyplot.figure()
for group in groups:
pyplot.subplot(len(groups), 1, i)
pyplot.plot(values[:, group])
pyplot.title(dataset.columns[group], y=0.5, loc='right')
i += 1
pyplot.show()
In [ ]:
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
data_file = '../data/pollution.csv'
# 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
In [7]:
# load dataset
dataset = read_csv(data_file, 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())
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# split into train and test sets
values = reframed.values
n_train_hours = 365 * 24 * 2
train = values[:n_train_hours, :]
val = values[n_train_hours:, :]
# split into input and outputs
train_X, train_y = train[:, :-1], train[:, -1]
val_X, val_y = val[:, :-1], val[:, -1]
# reshape input to be 3D [samples, timesteps, features]
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
val_X = val_X.reshape((val_X.shape[0], 1, val_X.shape[1]))
print(train_X.shape, train_y.shape, val_X.shape, val_y.shape)
In [9]:
import keras
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
In [12]:
default_optimizer = keras.optimizers.Adagrad(lr=1e-4, epsilon=None, decay=0.0)
dropout = 0.75
nb_out = 1
# design network
model = Sequential()
model.add(LSTM(units = 1024,
input_shape=(train_X.shape[1], train_X.shape[2]),
return_sequences=True))
model.add(Dropout(dropout))
model.add(LSTM(units=256,
return_sequences=True))
model.add(Dropout(dropout))
model.add(LSTM(units=256,
return_sequences=True))
model.add(Dropout(dropout))
model.add(LSTM(units=64,
return_sequences=False))
model.add(Dropout(dropout))
model.add(Dense(units=nb_out,
activation='elu' #'sigmoid'
))
model.compile(loss='mae', # 'binary_crossentropy'
optimizer=default_optimizer,
metrics=['accuracy'])
print(model.summary())
In [15]:
import os
model_path = '../model/multivariate_LSTM.h5'
if os.path.exists(model_path):
model.load_weights(model_path)
In [ ]:
# fit network
history = model.fit(train_X, train_y,
epochs=50,
batch_size=72,
validation_data=(val_X,
val_y),
verbose=2,
shuffle=False,
callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=0,
patience=10,
verbose=0,
mode='min'),
keras.callbacks.ModelCheckpoint(model_path,monitor='val_loss',
save_best_only=True,
mode='min',
verbose=0)])
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# plot history
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='validation')
pyplot.legend()
pyplot.show()
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# make a prediction
yhat = model.predict(val_X)
test_X = val_X.reshape((val_X.shape[0], val_X.shape[2]))
# invert scaling for forecast
inv_yhat = concatenate((yhat, val_X[:, 1:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:,0]
# invert scaling for actual
val_y = val_y.reshape((len(val_y), 1))
inv_y = concatenate((val_y, val_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)