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naive_MLP_forecasting.py
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naive_MLP_forecasting.py
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from models import MLP
import util
import eval
from sklearn.preprocessing import MinMaxScaler
import time
import numpy as np
# np.random.seed(123)
def MLP_forecasting(dataset, inputDim, lr=1e-3, hiddenNum=50, outputDim=1, epoch=20, batchSize=30, plot_flag=False):
# normalize time series
# dataset = dataset.reshape(-1, 1)
scaler = MinMaxScaler(feature_range=(0.0, 1.0)).fit(dataset)
dataset = scaler.transform(dataset)
# divide the series into training/testing samples
# NOTE: Not RNN format
train, test = util.divideTrainTest(dataset)
trainX, trainY = util.createSamples(train, inputDim, RNN=False)
testX, testY = util.createSamples(test, inputDim, RNN=False)
print("trainX shape is", trainX.shape)
print("trainY shape is", trainY.shape)
print("testX shape is", testX.shape)
print("testY shape is", testY.shape)
# buil model and train
MLP_model = MLP.MLP_Model(inputDim, hiddenNum, outputDim, lr)
t1 = time.time()
MLP_model.train(trainX, trainY, epoch, batchSize)
t2 = time.time()-t1
print("train time is", t2)
# forecasting
trainPred = MLP_model.predict(trainX)
testPred = MLP_model.predict(testX)
# reverse the time series
trainPred = scaler.inverse_transform(trainPred)
trainY = scaler.inverse_transform(trainY)
testPred = scaler.inverse_transform(testPred)
testY = scaler.inverse_transform(testY)
# evaluate
MAE = eval.calcMAE(testY, testPred)
print("test MAE", MAE)
MRSE = eval.calcRMSE(testY, testPred)
print("test RMSE", MRSE)
SMAPE = eval.calcSMAPE(testY, testPred)
print("test SMAPE", SMAPE)
if plot_flag:
util.plot(trainPred, trainY, testPred, testY)
return trainPred, testPred, MAE, MRSE, SMAPE
if __name__ == "__main__":
lag = 40
batch_size = 32
epoch = 20
hidden_dim = 64
lr = 1e-4
# ts, data = util.load_data("./data/NSW2013.csv", columnName="TOTALDEMAND")
# ts, data = util.load_data("./data/bike_hour.csv", columnName="cnt")
# ts, data = util.load_data("./data/TAS2016.csv", columnName="TOTALDEMAND")
# ts, data = util.load_data("./data/traffic_data_in_bits.csv", columnName="value")
# ts, data = util.load_data("./data/beijing_pm25.csv", columnName="pm2.5")
ts, data = util.load_data("./data/pollution.csv", columnName="Ozone")
trainPred, testPred, mae, mrse, smape = MLP_forecasting(data, inputDim=lag, hiddenNum=hidden_dim,
lr=lr, epoch=epoch, batchSize=batch_size, plot_flag=True)