使用 OLS 回归（Python、StatsModels、Pandas）预测未来值

Question

我目前正在尝试在 Python 中实现 MLR，但不确定如何将找到的系数应用于未来值。

import pandas as pd
import statsmodels.formula.api as sm
import statsmodels.api as sm2

TV = [230.1, 44.5, 17.2, 151.5, 180.8]
Radio = [37.8,39.3,45.9,41.3,10.8]
Newspaper = [69.2,45.1,69.3,58.5,58.4]
Sales = [22.1, 10.4, 9.3, 18.5,12.9]
df = pd.DataFrame({'TV': TV, 
                   'Radio': Radio, 
                   'Newspaper': Newspaper, 
                   'Sales': Sales})

Y = df.Sales
X = df[['TV','Radio','Newspaper']]
X = sm2.add_constant(X)
model = sm.OLS(Y, X).fit()
>>> model.params
const       -0.141990
TV           0.070544
Radio        0.239617
Newspaper   -0.040178
dtype: float64

假设我想预测以下 DataFrame 的“销售额”：

EDIT

TV     Radio    Newspaper    Sales
230.1  37,8       69.2       22.4
44.5   39.3       45.1       10.1
...    ...        ...        ...
25      15        15
30      20        22
35      22        36

我一直在尝试在此处找到的方法，但似乎无法正常工作：Forecasting using Pandas OLS

谢谢！

Answer 1

假设 df2 是您的新样本数据帧：

model = sm.OLS(Y, X).fit()
new_x = df2.loc[df.Sales.notnull(), ['TV', 'Radio', 'Newspaper']].values
new_x = sm2.add_constant(new_x)  # sm2 = statsmodels.api
y_predict = model.predict(new_x)

>>> y_predict
array([ 4.61319034,  5.88274588,  6.15220225])

您可以将结果直接分配给 df2，如下所示：

df2.loc[:, 'Sales'] = model.predict(new_x)

要使用回归预测填充原始 DataFrame 中缺失的销售值，请尝试：

X = df.loc[df.Sales.notnull(), ['TV', 'Radio', 'Newspaper']]
X = sm2.add_constant(X)
Y = df[df.Sales.notnull()].Sales

model = sm.OLS(Y, X).fit()
new_x = df.loc[df.Sales.isnull(), ['TV', 'Radio', 'Newspaper']]
new_x = sm2.add_constant(new_x)  # sm2 = statsmodels.api

df.loc[df.Sales.isnull(), 'Sales'] = model.predict(new_x)