python - sklearn.linear_model.ridge 中的统计汇总表？

Question

在 OLS 形式的 StatsModels 中，results.summary 显示回归结果的汇总(例如 AIC、BIC、R 平方、...)

有什么办法可以在 sklearn.linear_model.ridge 中得到这个汇总表吗？

如果有人能指导我，我将不胜感激。谢谢。

Answer 1

据我所知，sklearn 中没有类似 R(或 Statsmodels)的汇总表。 (请勾选this answer)

相反，如果您需要它，可以使用 statsmodels.regression.linear_model.OLS.fit_regularized类(class)。 (L1_wt=0 用于岭回归。)

目前，似乎 model.fit_regularized(~).summary() 返回 None 尽管有下面的文档字符串。但是对象有params，summary()可以不知何故使用。

Returns: A RegressionResults object, of the same type returned by fit.

示例。

样本数据不适用于岭回归，但无论如何我都会尝试。

在.

import numpy as np
import pandas as pd
import statsmodels
import statsmodels.api as sm
import matplotlib.pyplot as plt

statsmodels.__version__

出去。

'0.8.0rc1'

在.

data = sm.datasets.ccard.load()

print "endog: " + data.endog_name
print "exog: " + ', '.join(data.exog_name)

data.exog[:5, :]

出去。

endog: AVGEXP
exog: AGE, INCOME, INCOMESQ, OWNRENT
Out[2]:
array([[ 38.    ,   4.52  ,  20.4304,   1.    ],
       [ 33.    ,   2.42  ,   5.8564,   0.    ],
       [ 34.    ,   4.5   ,  20.25  ,   1.    ],
       [ 31.    ,   2.54  ,   6.4516,   0.    ],
       [ 32.    ,   9.79  ,  95.8441,   1.    ]])

在.

y, X = data.endog, data.exog

model = sm.OLS(y, X)
results_fu = model.fit()

print results_fu.summary()

出去。

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      y   R-squared:                       0.543
Model:                            OLS   Adj. R-squared:                  0.516
Method:                 Least Squares   F-statistic:                     20.22
Date:                Wed, 19 Oct 2016   Prob (F-statistic):           5.24e-11
Time:                        17:22:48   Log-Likelihood:                -507.24
No. Observations:                  72   AIC:                             1022.
Df Residuals:                      68   BIC:                             1032.
Df Model:                           4                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
x1            -6.8112      4.551     -1.497      0.139     -15.892       2.270
x2           175.8245     63.743      2.758      0.007      48.628     303.021
x3            -9.7235      6.030     -1.613      0.111     -21.756       2.309
x4            54.7496     80.044      0.684      0.496    -104.977     214.476
==============================================================================
Omnibus:                       76.325   Durbin-Watson:                   1.692
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              649.447
Skew:                           3.194   Prob(JB):                    9.42e-142
Kurtosis:                      16.255   Cond. No.                         87.5
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

在.

frames = []
for n in np.arange(0, 0.25, 0.05).tolist():
    results_fr = model.fit_regularized(L1_wt=0, alpha=n, start_params=results_fu.params)

    results_fr_fit = sm.regression.linear_model.OLSResults(model, 
                                                           results_fr.params, 
                                                           model.normalized_cov_params)
    frames.append(np.append(results_fr.params, results_fr_fit.ssr))

    df = pd.DataFrame(frames, columns=data.exog_name + ['ssr*'])
df.index=np.arange(0, 0.25, 0.05).tolist()
df.index.name = 'alpha*'
df.T

出去。

在.

%matplotlib inline

fig, ax = plt.subplots(1, 2, figsize=(14, 4))

ax[0] = df.iloc[:, :-1].plot(ax=ax[0])
ax[0].set_title('Coefficient')

ax[1] = df.iloc[:, -1].plot(ax=ax[1])
ax[1].set_title('SSR')

出去。

在.

results_fr = model.fit_regularized(L1_wt=0, alpha=0.04, start_params=results_fu.params)
final = sm.regression.linear_model.OLSResults(model, 
                                              results_fr.params, 
                                              model.normalized_cov_params)

print final.summary()

出去。

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      y   R-squared:                       0.543
Model:                            OLS   Adj. R-squared:                  0.516
Method:                 Least Squares   F-statistic:                     20.17
Date:                Wed, 19 Oct 2016   Prob (F-statistic):           5.46e-11
Time:                        17:22:49   Log-Likelihood:                -507.28
No. Observations:                  72   AIC:                             1023.
Df Residuals:                      68   BIC:                             1032.
Df Model:                           4                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
x1            -5.6375      4.554     -1.238      0.220     -14.724       3.449
x2           159.1412     63.781      2.495      0.015      31.867     286.415
x3            -8.1360      6.034     -1.348      0.182     -20.176       3.904
x4            44.2597     80.093      0.553      0.582    -115.564     204.083
==============================================================================
Omnibus:                       76.819   Durbin-Watson:                   1.694
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              658.948
Skew:                           3.220   Prob(JB):                    8.15e-144
Kurtosis:                      16.348   Cond. No.                         87.5
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.