在 statsmodels 的摘要中保留变量名称
Preserve variable names in summary from statsmodels
我正在使用来自 statsmodel 的 OLS，link 是 https://www.statsmodels.org/stable/examples/notebooks/generated/ols.html
#USD
X = sm.add_constant(USD)
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
OLS Regression Results
========================================================================================
Dep. Variable: All Ordinaries closing price R-squared: 0.265
Model: OLS Adj. R-squared: 0.265
Method: Least Squares F-statistic: 352.4
Date: Tue, 23 Oct 2018 Prob (F-statistic): 2.35e-67
Time: 17:30:24 Log-Likelihood: -8018.8
No. Observations: 977 AIC: 1.604e+04
Df Residuals: 975 BIC: 1.605e+04
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 1843.1414 149.675 12.314 0.000 1549.418 2136.864
USD 3512.5040 187.111 18.772 0.000 3145.318 3879.690
==============================================================================
Omnibus: 276.458 Durbin-Watson: 0.009
Prob(Omnibus): 0.000 Jarque-Bera (JB): 74.633
Skew: 0.438 Prob(JB): 6.22e-17
Kurtosis: 1.967 Cond. No. 10.7
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified
您可以看到摘要中的 X 显示为美元，这正是我想要的。
但是，添加新变量后
#JPY + USD
X = sm.add_constant(JPY)
X = np.column_stack((X, USD))
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
OLS Regression Results
========================================================================================
Dep. Variable: All Ordinaries closing price R-squared: 0.641
Model: OLS Adj. R-squared: 0.640
Method: Least Squares F-statistic: 868.8
Date: Tue, 23 Oct 2018 Prob (F-statistic): 2.80e-217
Time: 17:39:19 Log-Likelihood: -7669.4
No. Observations: 977 AIC: 1.534e+04
Df Residuals: 974 BIC: 1.536e+04
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -1559.5880 149.478 -10.434 0.000 -1852.923 -1266.253
x1 78.6589 2.466 31.902 0.000 73.820 83.497
x2 -366.5850 178.672 -2.052 0.040 -717.211 -15.958
==============================================================================
Omnibus: 24.957 Durbin-Watson: 0.031
Prob(Omnibus): 0.000 Jarque-Bera (JB): 27.278
Skew: 0.353 Prob(JB): 1.19e-06
Kurtosis: 3.415 Cond. No. 743.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
它没有显示美元和日元，而是 x1 x2。有办法解决吗？我尝试了 google 但一无所获。
因为我的问题都是关心显示，所以，如果我保留header，那么问题就解决了，所以我post我的解决方案，以防有人遇到同样的问题。
#JPY + USD
X = JPY.join(USD)
X = sm.add_constant(X)
#X = np.column_stack((X, USD))
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
OLS Regression Results
========================================================================================
Dep. Variable: All Ordinaries closing price R-squared: 0.641
Model: OLS Adj. R-squared: 0.640
Method: Least Squares F-statistic: 868.8
Date: Tue, 23 Oct 2018 Prob (F-statistic): 2.80e-217
Time: 22:51:43 Log-Likelihood: -7669.4
No. Observations: 977 AIC: 1.534e+04
Df Residuals: 974 BIC: 1.536e+04
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -1559.5880 149.478 -10.434 0.000 -1852.923 -1266.253
JPY 78.6589 2.466 31.902 0.000 73.820 83.497
USD -366.5850 178.672 -2.052 0.040 -717.211 -15.958
==============================================================================
Omnibus: 24.957 Durbin-Watson: 0.031
Prob(Omnibus): 0.000 Jarque-Bera (JB): 27.278
Skew: 0.353 Prob(JB): 1.19e-06
Kurtosis: 3.415 Cond. No. 743.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
这是使用 pandas 的简单修复。您只需要在 summary() 中添加一个功能列表。
# list of features (names)
features = list(df.iloc[:, 0:-1].columns) # exclude last column (label)
# scale features
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# train MLR model
regressor = sm.OLS(y_train, X_train).fit()
regressor.summary(xname=features)
我正在使用来自 statsmodel 的 OLS，link 是 https://www.statsmodels.org/stable/examples/notebooks/generated/ols.html
#USD
X = sm.add_constant(USD)
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
OLS Regression Results
========================================================================================
Dep. Variable: All Ordinaries closing price R-squared: 0.265
Model: OLS Adj. R-squared: 0.265
Method: Least Squares F-statistic: 352.4
Date: Tue, 23 Oct 2018 Prob (F-statistic): 2.35e-67
Time: 17:30:24 Log-Likelihood: -8018.8
No. Observations: 977 AIC: 1.604e+04
Df Residuals: 975 BIC: 1.605e+04
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 1843.1414 149.675 12.314 0.000 1549.418 2136.864
USD 3512.5040 187.111 18.772 0.000 3145.318 3879.690
==============================================================================
Omnibus: 276.458 Durbin-Watson: 0.009
Prob(Omnibus): 0.000 Jarque-Bera (JB): 74.633
Skew: 0.438 Prob(JB): 6.22e-17
Kurtosis: 1.967 Cond. No. 10.7
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified
您可以看到摘要中的 X 显示为美元，这正是我想要的。 但是，添加新变量后
#JPY + USD
X = sm.add_constant(JPY)
X = np.column_stack((X, USD))
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
OLS Regression Results
========================================================================================
Dep. Variable: All Ordinaries closing price R-squared: 0.641
Model: OLS Adj. R-squared: 0.640
Method: Least Squares F-statistic: 868.8
Date: Tue, 23 Oct 2018 Prob (F-statistic): 2.80e-217
Time: 17:39:19 Log-Likelihood: -7669.4
No. Observations: 977 AIC: 1.534e+04
Df Residuals: 974 BIC: 1.536e+04
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -1559.5880 149.478 -10.434 0.000 -1852.923 -1266.253
x1 78.6589 2.466 31.902 0.000 73.820 83.497
x2 -366.5850 178.672 -2.052 0.040 -717.211 -15.958
==============================================================================
Omnibus: 24.957 Durbin-Watson: 0.031
Prob(Omnibus): 0.000 Jarque-Bera (JB): 27.278
Skew: 0.353 Prob(JB): 1.19e-06
Kurtosis: 3.415 Cond. No. 743.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
它没有显示美元和日元，而是 x1 x2。有办法解决吗？我尝试了 google 但一无所获。
因为我的问题都是关心显示，所以，如果我保留header，那么问题就解决了，所以我post我的解决方案，以防有人遇到同样的问题。
#JPY + USD
X = JPY.join(USD)
X = sm.add_constant(X)
#X = np.column_stack((X, USD))
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
OLS Regression Results
========================================================================================
Dep. Variable: All Ordinaries closing price R-squared: 0.641
Model: OLS Adj. R-squared: 0.640
Method: Least Squares F-statistic: 868.8
Date: Tue, 23 Oct 2018 Prob (F-statistic): 2.80e-217
Time: 22:51:43 Log-Likelihood: -7669.4
No. Observations: 977 AIC: 1.534e+04
Df Residuals: 974 BIC: 1.536e+04
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -1559.5880 149.478 -10.434 0.000 -1852.923 -1266.253
JPY 78.6589 2.466 31.902 0.000 73.820 83.497
USD -366.5850 178.672 -2.052 0.040 -717.211 -15.958
==============================================================================
Omnibus: 24.957 Durbin-Watson: 0.031
Prob(Omnibus): 0.000 Jarque-Bera (JB): 27.278
Skew: 0.353 Prob(JB): 1.19e-06
Kurtosis: 3.415 Cond. No. 743.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
这是使用 pandas 的简单修复。您只需要在 summary() 中添加一个功能列表。
# list of features (names)
features = list(df.iloc[:, 0:-1].columns) # exclude last column (label)
# scale features
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# train MLR model
regressor = sm.OLS(y_train, X_train).fit()
regressor.summary(xname=features)