这是我为应用多元线性回归而构建的代码。我添加了标准缩放器来修复 Y 截距 p 值，该值并不显着，但问题是 CV RMSE 的结果最终发生了变化并且不再有意义，并且在绘制相关矩阵的代码中收到错误说： 属性错误： 'numpy.ndarray' 对象没有属性 'corr'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
from scipy import stats
from scipy.stats.stats import pearsonr
# Import Excel File
data = pd.read_excel("C:\Users\AchourAh\Desktop\Multiple_Linear_Regression\SP Level Reasons Excels\SP000273701_PL14_IPC_03_09_2018_Reasons.xlsx",'Sheet1') #Import Excel file
# Replace null values of the whole dataset with 0
data1 = data.fillna(0)
print(data1)
# Extraction of the independent and dependent variables
X = data1.iloc[0:len(data1),[1,2,3,4,5,6,7]] #Extract the column of the COPCOR SP we are going to check its impact
Y = data1.iloc[0:len(data1),9] #Extract the column of the PAUS SP
# Data Splitting to train and test set
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size =0.25,random_state=1)
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
# Statistical Analysis of the training set with Statsmodels
X = sm.add_constant(X_train) # add a constant to the model
est = sm.OLS(Y_train, X).fit()
print(est.summary()) # print the results
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import math
lm = LinearRegression() # create an lm object of LinearRegression Class
lm.fit(X_train,Y_train) # train our LinearRegression model using the training set of data - dependent and independent variables as parameters. Teaching lm that Y_train values are all corresponding to X_train.
print(lm.intercept_)
print(lm.coef_)
mse_test = mean_squared_error(Y_test, lm.predict(X_test))
print(math.sqrt(mse_test))
# Data Splitting to train and test set of the reduced data
X_1 = data1.iloc[0:len(data1),[1,2]] #Extract the column of the COPCOR SP we are going to check its impact
X_train2, X_test2, Y_train2, Y_test2 = train_test_split(X_1, Y, test_size =0.25,random_state=1)
X_train2 = ss.fit_transform(X_train2)
X_test2 = ss.transform(X_test2)
# Statistical Analysis of the reduced model with Statsmodels
X_reduced = sm.add_constant(X_train2) # add a constant to the model
est_reduced = sm.OLS(Y_train2, X_reduced).fit()
print(est_reduced.summary()) # print the results
# Fitting a Linear Model for the reduced model with Scikit-Learn
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import math
lm1 = LinearRegression() #create an lm object of LinearRegression Class
lm1.fit(X_train2, Y_train2)
print(lm1.intercept_)
print(lm1.coef_)
mse_test1 = mean_squared_error(Y_test2, lm1.predict(X_test2))
print(math.sqrt(mse_test1))
#Cross Validation and Training again the model
from sklearn.model_selection import KFold
from sklearn import model_selection
kf = KFold(n_splits=6, random_state=1)
for train_index, test_index in kf.split(X_train2):
print("Train:", train_index, "Validation:",test_index)
X_train1, X_test1 = X[train_index], X[test_index]
Y_train1, Y_test1 = Y[train_index], Y[test_index]
results = -1 * model_selection.cross_val_score(lm1, X_train1, Y_train1,scoring='neg_mean_squared_error', cv=kf)
print(np.sqrt(results))
#RMSE values interpretation
print(math.sqrt(mse_test1))
print(math.sqrt(results.mean()))
#Good model built no overfitting or underfitting (Barely Same for test and training :Goal of Cross validation but low prediction accuracy = Value is big
import seaborn
Corr=X_train2.corr(method='pearson')
mask=np.zeros_like(Corr)
mask[np.triu_indices_from(mask)]=True
seaborn.heatmap(Corr,cmap='RdYlGn_r',vmax=1.0,vmin=-1.0,mask=mask, linewidths=2.5)
plt.yticks(rotation=0)
plt.xticks(rotation=90)
plt.show()
enter code here
您知道如何解决问题吗？
我猜问题出在：
Corr=X_train2.corr(method='pearson')
.corr是一个熊猫数据帧方法，但X_train2在这个阶段是一个numpy数组。如果将数据帧/系列传递到标准缩放器，则返回一个 numpy 数组。尝试将上述内容替换为：
Corr=pd.DataFrame(X_train2).corr(method='pearson')
或者以各自的形式使用 numpy.corrcoef 或 numpy.correlate 。