问题
Suppose I have a pandas
dataframe:
df = pd.DataFrame({'x1': [0, 1, 2, 3, 4],
'x2': [10, 9, 8, 7, 6],
'x3': [.1, .1, .2, 4, 8],
'y': [17, 18, 19, 20, 21]})
Now I fit a statsmodels
model using a formula (which uses patsy
under the hood):
import statsmodels.formula.api as smf
fit = smf.ols(formula='y ~ x1:x2', data=df).fit()
What I want is a list of the columns of df
that fit
depends on, so that I can use fit.predict()
on another dataset. If I try list(fit.params.index)
, for example, I get:
['Intercept', 'x1:x2']
I've tried recreating the patsy design matrix, and using design_info
, but I still only ever get x1:x2
. What I want is:
['x1', 'x2']
Or even:
['Intercept', 'x1', 'x2']
How can I get this from just the fit
object?
回答1:
Simply test if the column names appear in the string representation of the formula:
ols = smf.ols(formula='y ~ x1:x2', data=df)
fit = ols.fit()
print([c for c in df.columns if c in ols.formula])
['x1', 'x2', 'y']
There is another approach by reconstructing the patsy model (more verbose, but also more reliable) and it does not depend on the original data frame:
md = patsy.ModelDesc.from_formula(ols.formula)
termlist = md.rhs_termlist + md.lhs_termlist
factors = []
for term in termlist:
for factor in term.factors:
factors.append(factor.name())
print(factors)
['x1', 'x2', 'y']
回答2:
predict
takes the same structure of data frame or a dictionary, and a call patsy converts it in a compatible way. To replicate this you can also check the code in statsmodels.base.model.Results.predict
the core of which is
exog = dmatrix(self.model.data.design_info.builder,
exog, return_type="dataframe")
The formula information itself is stored in the description of the terms
in design_info
. The variable names itself are used in summary()
and as index in the returned pandas Series for example in results.params
.
来源:https://stackoverflow.com/questions/43378033/how-do-i-get-the-columns-that-a-statsmodels-patsy-formula-depends-on