Python Statsmodels:OLS 回归器无法预测 [英] Python Statsmodels: OLS regressor not predicting

问题描述

我编写了以下代码，但我无法使预测"方法起作用:

I wrote the following piece of code but I just cannot get the 'predict' method to work:

import statsmodels.api as sm
from statsmodels.formula.api import ols
ols_model = ols('Consumption ~ Disposable_Income', df).fit()

我的df"是一个 Pandas 数据框，列标题为Consumption"和Disposable_Income".例如，当我跑步时，

My 'df' is a pandas dataframe with column headings 'Consumption' and 'Disposable_Income'. When I run, for example,

ols_model.predict([1000.0])

我得到:类型错误:列表索引必须是整数，而不是 str"

I get: "TypeError: list indices must be integers, not str"

例如，当我跑步时

ols_model.predict(df['Disposable_Income'].values)

我得到:IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) 和整数或布尔数组是有效的索引"

I get: "IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices"

我很困惑，因为我认为这两种格式正是文档所说的 - 放入 x 变量的值数组.我到底应该如何使用预测"方法?

I'm very confused because I thought these two formats are precisely what the documentation says - put in an array of values for the x variable. How exactly am I supposed to use the 'predict' method?

这就是我的 df 的样子:

This is how my df look:

推荐答案

由于您使用模型中的公式，因此公式信息也将用于 predict 中 exog 的解释.

Since you work with the formulas in the model, the formula information will also be used in the interpretation of the exog in predict.

我认为您需要使用具有正确解释变量名称的数据框或字典.

I think you need to use a dataframe or a dictionary with the correct name of the explanatory variable(s).

ols_model.predict({'Disposable_Income':[1000.0]})

或类似的东西

df_predict = pd.DataFrame([[1000.0]], columns=['Disposable_Income'])
ols_model.predict(df_predict)

如果用于预测的完整设计矩阵(包括常数)可用，则另一种选择是避免预测中的公式处理

Another option is to avoid formula handling in predict if the full design matrix for prediction, including constant, is available

AFAIR，这也应该有效:

AFAIR, this should also work:

ols_model.predict([[1, 1000.0]], transform=False)

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