運行以下代碼時出現錯誤（'DataFrame' 對象沒有屬性 'as_matrix'）

Question

基本 LSTM 模型導入 keras 庫

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())

拆分訓練和測試數據集並展開 lstm 模型的訓練和測試數據

    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)

功能定義

將數據集拆分為長短期記憶模型的訓練和測試特征

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

使用不同的窗口進行測試和訓練，以防止數據中的信息泄漏

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)

錯誤

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

我錯誤地導入了這個 sklearn.preprocessing.StandardScaler。 刪除這行代碼后，一切順利