假设我有一个pandas数据框:
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]})现在,我使用公式(在引擎盖下使用patsy)拟合了statsmodels模型:
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()我想要的是fit所依赖的df列的列表,以便可以在另一个数据集上使用fit.predict().例如,如果尝试list(fit.params.index),我将得到:
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']我尝试重新创建patsy设计矩阵,并使用design_info,但我仍然只能得到x1:x2.我想要的是:
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']甚至:
['Intercept', 'x1', 'x2']如何仅从fit对象获得此信息?
How can I get this from just the fit object?
推荐答案简单地测试列名称是否出现在公式的字符串表示形式中:
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']还有另一种方法可以通过重建patsy模型(更详细,但也更可靠),并且它不依赖于原始数据帧:
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']更多推荐
如何获取statsmodels/patsy公式所依赖的列?
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