对多列执行 LSTM

Question

I have one column of sequential demand for one item:

I have a LSTM neural network to test the prediction ability of this network and it works for one column. See the code below. But now I want to use several columns for different items and calculate the 'ABSE' for every column.

How can I do this?

This is the code:

  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)
  # load the dataset
  dataframe = pandas.read_excel('dataset.xlsx')
  dataset = dataframe.values
 dataset = dataset.astype('float32')
  # normalize the dataset
  scaler = MinMaxScaler(feature_range=(0, 1))
  dataset = scaler.fit_transform(dataset)
  # 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),:]
 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)
 look_back = 1
 trainX, trainY = create_dataset(train, look_back)
 testX, testY = create_dataset(test, look_back)
 trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
 testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
 # create and fit the LSTM network
 model = Sequential()
 model.add(LSTM(4, input_shape=(1, look_back)))
 model.add(Dense(1))
 model.compile(loss='mean_squared_error', optimizer='adam')
 model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=0)
 def ABSE(a,b):
     ABSE = abs((b-a)/a)
     return numpy.mean(ABSE)
 # 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 = ABSE(trainY[0], trainPredict[:,0])
 print('Train Score: %.2f ABSE' % (trainScore))
 testScore = ABSE(testY[0], testPredict[:,0])
 print('Test Score: %.2f ABSE' % (testScore))

Answer 1

If your code works for the first column. Just extract column separately and give it to the model. Like this :

# train your model on first column
...
for name_col in dataframe.columns:
    item = dataframe[name_col]

    # your preprocessing
    ...

    model.predict(item)

    # your evaluation
    ...