Question

我正在尝试使用Python中的 Statsmodels 进行一些多元线性回归，但我在尝试组织数据方面遇到了一些心理障碍。

因此默认的Boston数据集如下所示：

线性回归模型的输出是这样的：

我的原始数据是空格分隔的：

我已经能够将它安排到阵列中了：

有没有更多Python经验的人知道如何以与波士顿数据集类似的方式格式化我的数据，以便我可以轻松地预测我的回归模型吗？例如，设置与我的数据索引相对应的feature_names。

以下是我的原始数据的前几行供参考：

cycles         instructions   cache-references  cache-misses  branches     branch-misses  page-faults  Power
62,206,703     32,245,343     611,044           95,558        5,641,681    222,594        421          6.6
77,401,927     61,320,289     822,194           98,898        10,910,837   595,585        1,392        6.1
344,672,658    271,884,884    5,371,884         1,253,294     49,628,843   2,782,476      5,392        7.6
231,536,106    173,069,386    3,239,546         325,881       31,584,329   1,777,599      4,372        7.0
212,658,828    152,965,489    3,100,104         251,128       28,182,710   1,588,984      4,285        6.8
1,222,008,914  1,254,822,100  21,562,804        647,512       228,200,750  8,455,056      5,044        15.6
932,484,581    1,132,190,670  8,591,598         507,549       196,773,155  7,610,639      7,147        12.5
241,069,403    148,143,290    3,745,890         320,577       27,384,544   1,614,852      4,325        7.4
253,961,868    195,947,891    3,399,113         331,988       36,069,348   1,980,045      4,322        7.7
142,030,480    91,300,650     2,026,211         242,980       17,269,376   1,010,190      3,651        6.5
90,317,329     51,421,629     1,309,714         146,585       9,332,184    492,279        1,511        6.2
293,537,472    224,121,684    3,964,357         379,418       41,137,776   1,981,583      3,386        7.9

由于

Answer 1

我会使用pandas将数据读入内存，否则只需按照您在波士顿住房价格中找到的示例：

import pandas as pd
import statsmodels.api as sm

df = pd.read_csv('data.txt', sep='\s+', thousands=',')
X = df.loc[:, 'cycles':'page-faults']
y = df['Power']
model = sm.OLS(y, X).fit()

在这种情况下，model.summary()变为

OLS Regression Results                            
==============================================================================
Dep. Variable:                  Power   R-squared:                       0.972
Model:                            OLS   Adj. R-squared:                  0.932
Method:                 Least Squares   F-statistic:                     24.56
Date:                Fri, 10 Nov 2017   Prob (F-statistic):            0.00139
Time:                        22:09:47   Log-Likelihood:                -21.470
No. Observations:                  12   AIC:                             56.94
Df Residuals:                       5   BIC:                             60.33
Df Model:                           7                                         
Covariance Type:            nonrobust                                         
====================================================================================
                       coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------
cycles            1.287e-07   5.11e-08      2.518      0.053   -2.66e-09     2.6e-07
instructions     -7.083e-09   4.21e-07     -0.017      0.987   -1.09e-06    1.07e-06
cache-references -1.625e-06   2.48e-06     -0.656      0.541   -7.99e-06    4.74e-06
cache-misses      3.222e-06   5.24e-06      0.615      0.566   -1.03e-05    1.67e-05
branches          1.281e-07    2.6e-06      0.049      0.963   -6.55e-06    6.81e-06
branch-misses    -1.625e-05    1.2e-05     -1.357      0.233    -4.7e-05    1.45e-05
page-faults          0.0016      0.002      0.924      0.398      -0.003       0.006
==============================================================================
Omnibus:                        2.485   Durbin-Watson:                   1.641
Prob(Omnibus):                  0.289   Jarque-Bera (JB):                0.787
Skew:                           0.606   Prob(JB):                        0.675
Kurtosis:                       3.326   Cond. No.                     1.92e+06
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.92e+06. This might indicate that there are
strong multicollinearity or other numerical problems.'

Statsmodels线性回归的格式数据

1 个答案: