LSTM模型预测出错

LSTM多变量时间序列代码
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dense, Dropout
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import seaborn as sns

df=pd.read_csv("train.csv",parse_dates=["Date"],index_col=[0])
df.shape
df.head()
df.tail()
test_split=round(len(df)*0.20)
test_split
df_for_training=df[:-1041]
df_for_testing=df[-1041:]
scaler = MinMaxScaler(feature_range=(0,1))
df_for_training_scaled = scaler.fit_transform(df_for_training)
#scaler = MinMaxScaler(feature_range=(0,1))
df_for_testing_scaled=scaler.transform(df_for_testing)

def createXY(dataset,n_past):
dataX = []
dataY = []
for i in range(n_past, len(dataset)):
dataX.append(dataset[i - n_past:i, 0:dataset.shape[1]])
dataY.append(dataset[i,0])
return np.array(dataX),np.array(dataY)
trainX,trainY=createXY(df_for_training_scaled,30)
testX,testY=createXY(df_for_testing_scaled,30)

from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import GridSearchCV

def build_model(optimizer):
grid_model = Sequential()
grid_model.add(LSTM(50,return_sequences=True,input_shape=(30,5)))
grid_model.add(LSTM(50))
grid_model.add(Dropout(0.2))
grid_model.add(Dense(1))

grid_model.compile(loss = 'mse',optimizer = optimizer)
return grid_model

grid_model = KerasRegressor(build_fn=build_model,verbose=1,validation_data=(testX,testY))

parameters = {'batch_size' : [16,20],
'epochs' : [8,10],
'optimizer' : ['adam','Adadelta'] }

grid_search = GridSearchCV(estimator = grid_model,
param_grid = parameters,
cv = 2)

#grid_search = grid_search.fit(trainX,trainY)
grid_search = grid_search.fit(trainX,trainY)
#grid_search.best_params
my_model=grid_search.best_estimator_.model
prediction=my_model.predict(testX)
print("prediction\n", prediction)
print("\nPrediction Shape-",prediction.shape)

scaler.inverse_transform(prediction)
prediction_copies_array = np.repeat(prediction,5, axis=-1)
pred=scaler.inverse_transform(np.reshape(prediction_copies_array,(len(prediction),5)))[:,0]

original_copies_array = np.repeat(testY,5, axis=-1)

original_copies_array.shape

original=scaler.inverse_transform(np.reshape(original_copies_array,(len(testY),5)))[:,0]

import matplotlib.pyplot as plt
plt.plot(original, color = 'red', label = 'Real Stock Price')
plt.plot(pred, color = 'blue', label = 'Predicted Stock Price')
plt.title(' Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel(' Stock Price')
plt.legend()
plt.show()

df_30_days_past=df.iloc[-30:,:]
df_30_days_future=pd.read_csv("test.csv",parse_dates=["Date"],index_col=[0])
df_30_days_future["Open"]=0
df_30_days_future=df_30_days_future[["Open","High","Low","Close","Adj Close"]]
old_scaled_array=scaler.transform(df_30_days_past)
new_scaled_array=scaler.transform(df_30_days_future)
new_scaled_df=pd.DataFrame(new_scaled_array)
new_scaled_df.iloc[:,0]=np.nan
full_df=pd.concat([pd.DataFrame(old_scaled_array),new_scaled_df]).reset_index().drop(["index"],axis=1)
full_df_scaled_array=full_df.values
(60, 5)
all_data=[]
time_step=30
for i in range(time_step,len(full_df_scaled_array)):
data_x=[]
data_x.append(full_df_scaled_array[i-time_step:i,0:full_df_scaled_array.shape[1]])
data_x=np.array(data_x)
prediction=my_model.predict(data_x)
all_data.append(prediction)
full_df.iloc[i,0]=prediction

new_array=np.array(all_data)
new_array=new_array.reshape(-1,1)
prediction_copies_array = np.repeat(new_array,5, axis=-1)
y_pred_future_30_days = scaler.inverse_transform(np.reshape(prediction_copies_array,(len(new_array),5)))[:,0]

from tensorflow.keras.models import Model
from tensorflow.keras.models import load_model

my_model.save('Model_future_value.h5')
print('Model Saved!')
scaler
import pickle
scalerfile = 'scaler_model_future_value.pkl'
pickle.dump(scaler, open(scalerfile, 'wb'))

错误提示LFile "G:/时间序列/Multivariate-time-series-forecasting-using-LSTM-main/forecast.py", line 57, in
grid_search = grid_search.fit(trainX,trainY)
File "C:\Users\dell\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\model_selection_search.py", line 805, in fit
base_estimator = clone(self.estimator)
File "C:\Users\dell\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\base.py", line 92, in clone
"either does not set or modifies parameter %s" % (estimator, name)
RuntimeError: Cannot clone object <tensorflow.python.keras.wrappers.scikit_learn.KerasRegressor object at 0x0000017C9E8D5828>, as the constructor either does not set or modifies parameter validation_data

可能是在语句 grid_search.fit(trainX,trainY)中出错，试试将参数由数组改成嵌套元组。
参考：
https://datascience.stackexchange.com/questions/66341/cannot-clone-object-keras-wrappers-scikit-learn-kerasregressor-object-at-0x7fdc
a数组转元组的嵌套列表：
list(map(tuple,a)

model.fit(train_X, train_y, epochs=epoch_num, batch_size=batch_size_num, validation_data=(test_X, test_y), verbose=2, shuffle=False)
参数不够，需要设置验证集数据,更为具体的参数可以看

Keras model.fit()参数详解_花木兰-CSDN博客_keras.model.fit fit(x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_ep... https://blog.csdn.net/weixin_40161254/article/details/102519416

你好，问题解决了吗？

您好，请问您解决了吗？

解决了吗

降低下sklearn 的版本看看

install scikit-learn==0.21.2