运行以下代码时出现错误（'DataFrame' 对象没有属性 'as_matrix'）

Question

Basic LSTM Model Import keras libraries

import math
import pandas as pd
import numpy as np
from IPython.display import display

from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from keras.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold

import lstm, time #helper libraries

import visualize as vs
import stock_data as sd
import LinearRegressionModel

stocks = pd.read_csv('E:/DBSOM DATA\FOM_Sem 2/Analyses of S&U Data/Project work/Stock-Price-Prediction-master/google_preprocessed.csv')
stocks_data = stocks.drop(['Item'], axis =1)

display(stocks_data.head())

Spliting train and test data sets and Unroll train and test data for lstm model

    X_train, X_test,y_train, y_test = sd.train_test_split_lstm(stocks_data, 5)

    unroll_length = 50
    X_train = sd.unroll(X_train, unroll_length)
    X_test = sd.unroll(X_test, unroll_length)
    y_train = y_train[-X_train.shape[0]:]
    y_test = y_test[-X_test.shape[0]:]

    print("x_train", X_train.shape)
    print("y_train", y_train.shape)
    print("x_test", X_test.shape)
    print("y_test", y_test.shape)

Function definition

Split the data set into training and testing feature for Long Short Term Memory Model

def train_test_split_lstm(stocks, prediction_time=1, test_data_size=450, unroll_length=50):  

# training data
test_data_cut = test_data_size + unroll_length + 1
x_train = stocks[0:-prediction_time - test_data_cut].values
y_train = stocks[prediction_time:-test_data_cut]['Close'].values

# test data
x_test = stocks[0 - test_data_cut:-prediction_time].values
y_test = stocks[prediction_time - test_data_cut:]['Close'].values
return x_train, x_test, y_train, y_test

Use different windows for testing and training to stop from leak of information in the data

def unroll(data, sequence_length=24):
result = []
for index in range(len(data) - sequence_length):
    result.append(data[index: index + sequence_length])
return np.asarray(result)

Error

AttributeError                            Traceback (most recent call last)
<ipython-input-52-59aa6ad29ad5> in <module>
----> 1 X_train, X_test,y_train, y_test = sd.train_test_split_lstm(stocks_data, 5)
  2 
  3 unroll_length = 50
  4 X_train = sd.unroll(X_train, unroll_length)
  5 X_test = sd.unroll(X_test, unroll_length)

~\Stock Price Prediction\stock_data.py in train_test_split_lstm(stocks, prediction_time, test_data_size, unroll_length)
 77     test_data_cut = test_data_size + unroll_length + 1
 78 
 ---> 79     x_train = stocks[0:-prediction_time - test_data_cut].to_numpy()
 80     y_train = stocks[prediction_time:-test_data_cut]['Close'].to_numpy()
 81 

~\anaconda3\envs\tensorflow\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
5128             if 
self._info_axis._can_hold_identifiers_and_holds_name(name):
5129                 return self[name]
-> 5130             return object.__getattribute__(self, name)
5131 
5132     def __setattr__(self, name: str, value) -> None:

AttributeError: 'DataFrame' object has no attribute 'as_matrix'

Answer 1

I have wrongly import this sklearn.preprocessing.StandardScaler. After removing this line of code, everything runs smooth