Fixed inference results when cate_feature_names is not defined
#225
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@kbattocchi I thought about that, but for that to work, we need a CATE estimator with linear final stage that will never have |
I think that one fairly straightforward approach would be to create a very simple wrapper like: class NoFtNamesEst:
def __init__(self, cate_est):
self.cate_est = clone(cate_est, safe=False)
def __getattr__(self, name):
if name != 'cate_feature_names`:
return getattr(self.cate_est, name)
else:
return self.__getattribute__(name)and then test that you can get the inference results with either a plain DML estimator or a wrapped one. |
| cls.T = np.random.normal(0, 1, size=(cls.n, )) | ||
| cls.Y = np.random.normal(0, 1, size=(cls.n, )) | ||
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| def test_inference_results(self): |
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I'd rename to something like test_inference_no_feature_names or something.
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I named it this way so we can add more tests under the same method in the future. And this is just one subtest.
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Okay; I generally prefer to have a larger number of fine grained tests than a smaller number of broader tests where possible because it makes it quicker to track down the nature of the error when there's a failure, but I don't feel super strongly about it.
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