多变量Python | Keras |预测
我有一个3列的数据集多变量Python | Keras |预测,python,tensorflow,machine-learning,keras,prediction,Python,Tensorflow,Machine Learning,Keras,Prediction,我有一个3列的数据集 |Date |Price |Sentiment | |---------------------------------| |Date 1 |Price 1 |S1 | |------------------------- -------| |Date 2 |Price 2 |S2 | |------------------------- -------| |Date 3 |Price 3
|Date |Price |Sentiment |
|---------------------------------|
|Date 1 |Price 1 |S1 |
|------------------------- -------|
|Date 2 |Price 2 |S2 |
|------------------------- -------|
|Date 3 |Price 3 |S3 |
|------------------------- -------|
在该数据集中,“价格”取决于“情绪”值,即如果情绪值为正,价格将上升,否则将下降。
我需要使用Keras预测“价格”。现在我使用中的代码来预测“价格”,但它没有考虑预测的“情绪”值
import numpy
import matplotlib.pyplot as plt
from pandas import read_csv
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
# convert an array of values into a dataset matrix
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)
# fix random seed for reproducibility
numpy.random.seed(7)
# load the dataset
dataframe = read_csv('./dataset/combined.csv', usecols=[1], engine='python')
dataset = dataframe.values
print (dataset)
dataset = dataset.astype('float64')
# 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),:]
# reshape into X=t and Y=t+1
look_back = 3
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], 1))
# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(look_back, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=1, batch_size=1, verbose=2)
# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# invert predictions
print (trainPredict)
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 = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
#print('Train Score: %.2f RMSE' % (trainScore))
#testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
#print('Test Score: %.2f RMSE' % (testScore))
# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
如何通过同时考虑“价格”和“情绪”列来预测“价格”?首先要做的是实际加载相关列,所以
dataframe = read_csv('./dataset/combined.csv', usecols=[1], engine='python')
到
我没有看到列被显式引用,所以我认为这应该足够了,但我可能遗漏了一些明显的东西。这导致了这个错误,'回溯(最近一次调用):trainPredict=scaler.inverse\u transform(trainPredict)文件中的第60行文件“complex\u prediction.py”“C:\Users\nidhi\Anaconda3\lib\site packages\sklearn\preprocessing\data.py”,第385行,在逆变换X-=self.min uuu值错误:具有形状(490,1)的不可广播输出操作数与广播形状(490,2)不匹配”
dataframe = read_csv('./dataset/combined.csv', usecols=[1, 2], engine='python')