获取错误(找到带有暗淡 3 的数组。运行以下代码时预期的估算器 <= 2)

Question

检查模型的稳健性：在本节中，我将检查LSTM模型的稳健性。从2017年7月1日到2017年7月份，我使用了新的未公开数据集。我已经从谷歌金融网站下载了数据集，以检查模型的稳健性。

import preprocess_data as ppd
data = pd.read_csv('E:/DBSOM DATAFOM_Sem 2/Analyses of S&U Data/Project work/Stock-Price-Prediction-master/googl.csv')
stocks = ppd.remove_data(data)
stocks = ppd.get_normalised_data(stocks)
stocks = stocks.drop(['Item'], axis = 1)
#Print the data frame head and tail
print(stocks.head())
X = stocks[:].values
Y = stocks[:]['Close'].values
X = sd.unroll(X,1)
Y = Y[-X.shape[0]:]
print(X.shape)
print(Y.shape)
# Generate predictions 
predictions = model.predict(X)
#get the test score
testScore = model.evaluate(X, Y, verbose=0)
print('Test Score: %.4f MSE (%.4f RMSE)' % (testScore, math.sqrt(testScore)))

函数定义

import pandas as pd
Import sklearn.preprocessing.StandardScaler
from sklearn.preprocessing import MinMaxScaler
def get_normalised_data(data):

# Initialize a scaler, then apply it to the features

scaler = MinMaxScaler()
numerical = ['Open', 'Close', 'Volume']
data[numerical] = scaler.fit_transform(data[numerical])
return data
def remove_data(data):
# Define columns of data to keep from historical stock data
item = []
open = []
close = []
volume = []
# Loop through the stock data objects backwards and store factors we want to keep
i_counter = 0
for i in range(len(data) - 1, -1, -1):
item.append(i_counter)
open.append(data['Open'][i])
close.append(data['Close'][i])
volume.append(data['Volume'][i])
i_counter += 1
# Create a data frame for stock data
stocks = pd.DataFrame()
# Add factors to data frame
stocks['Item'] = item
stocks['Open'] = open
stocks['Close'] = pd.to_numeric(close)
stocks['Volume'] = pd.to_numeric(volume)
# return new formatted data
return stocks

我花了很多时间来解决这个错误，但没有找到解决方案。请提出建议。

Answer 1

我删除了这行Import sklearn.preprrocessing.StandardScaler

还有volla。一切顺利。