使用 Python 和 Pandas 对具有不同列名的 statsmodels.formula 数据使用 predict()
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Using predict() on statsmodels.formula data with different column names using Python and Pandas
提问by kuzzooroo
I've got some regressions results from running statsmodels.formula.api.ols. Here's a toy example:
我从运行中得到了一些回归结果statsmodels.formula.api.ols。这是一个玩具示例：
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
example_df = pd.DataFrame(np.random.randn(10, 3))
example_df.columns = ["a", "b", "c"]
fit = smf.ols('a ~ b', example_df).fit()
I'd like to apply the model to column c, but a naive attempt to do so doesn't work:
我想将该模型应用于 column c，但天真地尝试这样做是行不通的：
fit.predict(example_df["c"])
Here's the exception I get:
这是我得到的例外：
PatsyError: Error evaluating factor: NameError: name 'b' is not defined
a ~ b
^
I can do something gross and create a new, temporary DataFramein which I rename the column of interest:
我可以做一些粗暴的事情并创建一个新的、临时的，DataFrame在其中重命名感兴趣的列：
example_df2 = pd.DataFrame(example_df["c"])
example_df2.columns = ["b"]
fit.predict(example_df2)
Is there a cleaner way to do this? (short of switching to statsmodels.apiinstead of statsmodels.formula.api)
有没有更干净的方法来做到这一点？（短切换到statsmodels.api而不是statsmodels.formula.api）
采纳答案by Josef
You can use a dictionary:
您可以使用字典：
>>> fit.predict({"b": example_df["c"]})
array([ 0.84770672, -0.35968269, 1.19592387, -0.77487812, -0.98805215,
0.90584753, -0.15258093, 1.53721494, -0.26973941, 1.23996892])
or create a numpy array for the prediction, although that is much more complicated if there are categorical explanatory variables:
或者为预测创建一个 numpy 数组，尽管如果有分类解释变量，这会复杂得多：
>>> fit.predict(sm.add_constant(example_df["c"].values), transform=False)
array([ 0.84770672, -0.35968269, 1.19592387, -0.77487812, -0.98805215,
0.90584753, -0.15258093, 1.53721494, -0.26973941, 1.23996892])
回答by Primer
If you replace your fitdefinition with this line:
如果你fit用这一行替换你的定义：
fit = smf.ols('example_df.a ~ example_df.b', example_df).fit()
It should work.
它应该工作。
fit.predict(example_df["c"])
array([-0.52664491, -0.53174346, -0.52172484, -0.52819856, -0.5253607 ,
-0.52391618, -0.52800043, -0.53350634, -0.52362988, -0.52520823])
